Package 'INLA'

Title: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations
Description: Full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion. It is a front-end to the inla-program.
Authors: Havard Rue [cre, aut], Finn Lindgren [aut], Elias Teixeira Krainski [aut], Sara Martino [ctb], Haakon Bakka [ctb], Daniel Simpson [ctb], Andrea Riebler [ctb], Geir-Arne Fuglstad [ctb], Cristian Chiuchiolo [ctb]
Maintainer: Havard Rue <hrue@r-inla.org>
License: MIT + file LICENSE
Version: 25.04.10
Built: 2025-06-28 21:27:16 UTC
Source: https://github.com/hrue/r-inla

Help Index

INLA

Description

Package to perform full Bayesian analysis on latent Gaussian models using Integrated Nested Laplace Approximations.

See https://www.r-inla.org/ for further details.

Usage

INLA()

Author(s)

Maintainer: Havard Rue hrue@r-inla.org

Authors:

Other contributors:

See Also

inla()

Convert sp objects to inla.mesh.segment objects.

Description

[Superseded] by fmesher::fm_as_segm()

Usage

as.inla.mesh.segment(sp, ...)

inla.sp2segment(sp, ...)

Arguments

sp

An sp polygon object of class Polygon, Polygons, SpatialPolygons, or SpatialPolygonsDataFrame.
...

Additional arguments passed on to fmesher::fm_as_segm().

Value

A inla.mesh.segment() object, or a list of inla.mesh.segment() objects.

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.segment()

Bivariate Meta Analysis

Description

Data are taken from a meta-analysis to compare the utility of three types of diagnostic imaging - lymphangiography (LAG), computed tomography (CT) and magnetic resonance (MR) - to detect lymph node metastases in patients with cervical cancer. The dataset consists of a total of 46 studies: the first 17 for LAG, the following 19 for CT and the last 10 for MR.

Usage

BivMetaAnalysis

Format

A data frame with 92 observations on the following 9 variables.

N

a numeric vector

Y

a numeric vector

diid

a numeric vector

lag.tp

a numeric vector

lag.tn

a numeric vector

ct.tp

a numeric vector

ct.tn

a numeric vector

mr.tp

a numeric vector

mr.tn

a numeric vector

References

J. Scheidler and H. Hricak and K. K. Yu and L. Subak and M. R. Segal,"Radiological evaluation of lymph node metastases in patients with cervical cancer: a meta-analysis",JAMA 1997

Examples

data(BivMetaAnalysis)

~~ data name/kind ... ~~

Description

~~ A concise (1-5 lines) description of the dataset. ~~

Format

A data frame with 6690 observations on the following 4 variables.

Y

Number of cases

N

a numeric vector

Age

a numeric vector

region

a numeric vector

References

Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall

cgeneric models

Description

A framework for defining latent models in C

Usage

inla.cgeneric.define(model = NULL, shlib = NULL, n = 0L, debug = FALSE, ...)

inla.cgeneric.q(cmodel = NULL)

Arguments

model

The name of the model function
shlib

Name of the compiled object-file with model
n

The size of the model
debug

Logical. Turn on/off debugging
...

Additional arguments, required by inla.cgeneric.define() to be named arguments
cmodel

The name of a cgeneric model-object (output from inla.cgeneric.define

Author(s)

Havard Rue hrue@r-inla.org

control.bgev

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.bgev(
  q.location = 0.5,
  q.spread = 0.25,
  q.mix = c(0.1, 0.2),
  beta.ab = 5L
)

inla.set.control.bgev.default(...)

Arguments

q.location

The quantile level for the location parameter
q.spread

The quantile level for the spread parameter (must be < 0.5)
q.mix

The lower and upper quantile level for the mixing function
beta.ab

The parameters a and b in the Beta mixing function
...

Named arguments passed on to the main function

Details

The control.bgev-list is set within the corresponding control.family-list as control parameters to the family="bgev"

See Also

Other control: control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.compute

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.compute(
  openmp.strategy = "default",
  hyperpar = TRUE,
  return.marginals = TRUE,
  return.marginals.predictor = FALSE,
  dic = FALSE,
  mlik = TRUE,
  cpo = FALSE,
  po = FALSE,
  waic = FALSE,
  residuals = FALSE,
  q = FALSE,
  config = FALSE,
  likelihood.info = FALSE,
  smtp = NULL,
  graph = FALSE,
  internal.opt = NULL,
  save.memory = NULL,
  control.gcpo = INLA::control.gcpo()
)

inla.set.control.compute.default(...)

Arguments

openmp.strategy

The computational strategy to use: 'small', 'medium', 'large', 'huge', 'default' and 'pardiso'.
hyperpar

A boolean variable if the marginal for the hyperparameters should be computed. Default TRUE.
return.marginals

A boolean variable if the marginals for the latent field should be returned (although it is computed). Default TRUE
return.marginals.predictor

A boolean variable if the marginals for the linear predictor should be returned (although it is computed). Default FALSE
dic

A boolean variable if the DIC-value should be computed. Default FALSE.
mlik

A boolean variable if the marginal likelihood should be computed. Default TRUE.
cpo

A boolean variable if the cross-validated predictive measures (cpo, pit) should be computed (default FALSE)
po

A boolean variable if the predictive ordinate should be computed (default FALSE)
waic

A boolean variable if the Watanabe-Akaike information criteria should be computed (default FALSE)
residuals

Provide estimates of residuals (whatever we mean by that). (default FALSE) Currently only residuals base on expected (saturated) deviance are available. The sign of the residuals are only ⁠very likely⁠ correct. These residuals are not properly justified from a Bayesian point of view, hence must be used with caution. It is provided in the hope they would be useful. This feature is EXPERIMENTAL for the moment, so changes can happen at any time.
q

A boolean variable if binary images of the precision matrix, the reordered precision matrix and the Cholesky triangle should be generated. (Default FALSE.)
config

A boolean variable if the internal GMRF approximations be stored. (Default FALSE.)
likelihood.info

A boolean variable to store likelihood-information or not. This option requires config=TRUE (Default FALSE. EXPERIMENTAL)
smtp

The sparse-matrix solver, one of 'default', 'taucs', 'band' or 'pardiso' (default inla.getOption("smtp")). smtp='pardiso' implies openmp.strategy='pardiso'.
graph

A boolean variable if the graph itself should be returned. (Default FALSE.)
internal.opt

A boolean variable, if to do internal online optimisations or not. (Default inla.getOption("internal.opt"))
save.memory

A boolean variable, make choices which saves memory over accuracy. (Default 'inla.getOption("save.memory")')
control.gcpo

(For experts only!) Set control variables for the gcpo. The intended use is to use inla.group.cv. Refer to control.gcpo, ?inla.group.cv and the vignette for details.
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.expert

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.expert(
  cpo.manual = FALSE,
  cpo.idx = -1,
  disable.gaussian.check = FALSE,
  jp = NULL,
  dot.product.gain = FALSE,
  globalconstr = list(A = NULL, e = NULL),
  opt.solve = FALSE
)

inla.set.control.expert.default(...)

Arguments

cpo.manual

A boolean variable to decide if the inla-program is to be runned in a manual-cpo-mode. (EXPERT OPTION: DO NOT USE)
cpo.idx

The index/indices of the data point(s) to remove. (EXPERIMENTAL OPTION: DO NOT USE)
disable.gaussian.check

Disable the check for fast computations with a Gaussian likelihood and identity link (default FALSE)
jp

An object of class inla.jp defining a joint prior
dot.product.gain

Output the gain in optimizing dot-products? (Default FALSE)
globalconstr

Add a global constraint (see ?f and argument extraconstr). Note that a global constraint does NOT correct the normalisation constant. (EXPERIMENTAL OPTION)
opt.solve

Store also L^T to optimize linear solves (TAUCS only). (EXPERIMENTAL OPTION: DO NOT USE)
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.family

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.family(
  dummy = 0,
  hyper = NULL,
  initial = NULL,
  prior = NULL,
  param = NULL,
  fixed = NULL,
  link = "default",
  sn.shape.max = 5,
  gev.scale.xi = 0.1,
  control.bgev = NULL,
  cenpoisson.I = c(-1L, -1L),
  beta.censor.value = 0,
  variant = 0L,
  link.simple = "default",
  control.mix = NULL,
  control.pom = NULL,
  control.link = INLA::control.link(),
  control.sem = NULL
)

inla.set.control.family.default(...)

Arguments

dummy

A dummy argument that can be used as a workaround
hyper

Definition of the hyperparameters
initial

(OBSOLETE!) Initial value for the hyperparameter(s) of the likelihood in the internal scale.
prior

(OBSOLETE!) The name of the prior distribution(s) for othe hyperparameter(s).
param

(OBSOLETE!) The parameters for the prior distribution
fixed

(OBSOLETE!) Boolean variable(s) to say if the hyperparameter(s) is fixed or random.
link

(OBSOLETE! Use control.link=list(model=) instead.) The link function to use.
sn.shape.max

Maximum value for the shape-parameter for Skew Normal observations (default 5.0)
gev.scale.xi

(Expert option, do not use unless you know what you are doing.) The internal scaling of the shape-parameter for the GEV distribution. (default 0.1)
control.bgev

See ?control.bgev
cenpoisson.I

The censoring interval for the censored Poisson
beta.censor.value

The censor value for the Beta-likelihood ⁠(0 <= beta.censor.value < 1/2)⁠
variant

This variable is used to give options for various variants of the likelihood, like chosing different parameterisations for example. See the relevant likelihood documentations for options (does only apply to some likelihoods).
link.simple

See inla.doc("0inflated")
control.mix

See ?control.mix
control.pom

See ?control.pom
control.link

See ?control.link
control.sem

Parameters for likelihood sem
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.fixed

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.fixed(
  cdf = NULL,
  quantiles = NULL,
  expand.factor.strategy = "model.matrix",
  mean = 0,
  mean.intercept = 0,
  prec = 0.001,
  prec.intercept = 0,
  compute = TRUE,
  correlation.matrix = FALSE,
  remove.names = NULL
)

inla.set.control.fixed.default(...)

Arguments

cdf

A list of values to compute the CDF for, for all fixed effects
quantiles

A list of quantiles to compute for all fixed effects
expand.factor.strategy

The strategy used to expand factors into fixed effects based on their levels. The default strategy is us use the model.matrix-function for which NA's are not allowed (expand.factor.strategy="model.matrix") and levels are possible removed. The alternative option (expand.factor.strategy="inla") use an inla-specific expansion which expand a factor into one fixed effects for each level, do allow for NA's and all levels are present in the model. In this case, factors MUST BE factors in the data.frame/list and NOT added as .+factor(x1)+. in the formula only.
mean

Prior mean for all fixed effects except the intercept. Alternatively, a named list with specific means where name=default applies to unmatched names. For example control.fixed=list(mean=list(a=1, b=2, default=0)) assign 'mean=1' to fixed effect 'a' , 'mean=2' to effect 'b' and 'mean=0' to all others. (default 0.0)
mean.intercept

Prior mean for the intercept (default 0.0)
prec

Default precision for all fixed effects except the intercept. Alternatively, a named list with specific means where name=default applies to unmatched names. For example control.fixed=list(prec=list(a=1, b=2, default=0.01)) assign 'prec=1' to fixed effect 'a' , 'prec=2' to effect 'b' and 'prec=0.01' to all others. (default 0.001)
prec.intercept

Default precision the intercept (default 0.0)
compute

Compute marginals for the fixed effects ? (default TRUE)
correlation.matrix

Compute the posterior correlation matrix for all fixed effects? (default FALSE) OOPS: This option will set up appropriate linear combinations and the results are shown as the posterior correlation matrix of the linear combinations. This option will imply control.inla=list(lincomb.derived.correlation.matrix=TRUE).
remove.names

A vector of names of expanded fixed effects to remove from the model-matrix. This is an expert option, and should only be used if you know what you are doing.
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.gcpo

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.gcpo(
  enable = FALSE,
  num.level.sets = -1,
  size.max = 32,
  strategy = c("posterior", "prior"),
  groups = NULL,
  selection = NULL,
  group.selection = NULL,
  friends = NULL,
  weights = NULL,
  verbose = FALSE,
  epsilon = 0.005,
  prior.diagonal = 1e-04,
  correct.hyperpar = TRUE,
  keep = NULL,
  remove = NULL,
  remove.fixed = TRUE
)

inla.set.control.gcpo.default(...)

Arguments

enable

TODO
num.level.sets

TODO
size.max

TODO
strategy

TODO
groups

TODO
selection

TODO
group.selection

TODO
friends

TODO
weights

TODO
verbose

TODO
epsilon

TODO
prior.diagonal

TODO
correct.hyperpar

TODO
keep

TODO
remove

TODO
remove.fixed

TODO
...

Named arguments passed on to the main function

Details

(For experts only!) Set control variables for the gcpo in control.compute. The intended use is to use inla.group.cv. Refer to ?inla.group.cv and the vignette for details.

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.group

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.group(
  model = "exchangeable",
  order = NULL,
  cyclic = FALSE,
  graph = NULL,
  scale.model = TRUE,
  adjust.for.con.comp = TRUE,
  hyper = NULL,
  initial = NULL,
  fixed = NULL,
  prior = NULL,
  param = NULL
)

inla.set.control.group.default(...)

Arguments

model

Group model (one of 'exchangable', 'exchangablepos', 'ar1', 'ar', 'rw1', 'rw2', 'besag', or 'iid')
order

Defines the order of the model: for model ar this defines the order p, in AR(p). Not used for other models at the time being.
cyclic

Make the group model cyclic? (Only applies to models 'ar1', 'rw1' and 'rw2')
graph

The graph specification (Only applies to model 'besag')
scale.model

Scale the intrinsic model (RW1, RW2, BESAG) so the generalized variance is 1. (Default TRUE)
adjust.for.con.comp

Adjust for connected components when scale.model=TRUE? (default TRUE)
hyper

Definition of the hyperparameter(s)
initial

(OBSOLETE!) The initial value for the group correlation or precision in the internal scale.
fixed

(OBSOLETE!) A boolean variable if the group correction or precision is assumed to be fixed or random.
prior

(OBSOLETE!) The name of the prior distribution for the group correlation or precision in the internal scale
param

(OBSOLETE!) Prior parameters
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.hazard

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.hazard(
  model = "rw1",
  hyper = NULL,
  fixed = FALSE,
  initial = NULL,
  prior = NULL,
  param = NULL,
  constr = TRUE,
  diagonal = NULL,
  n.intervals = 15,
  cutpoints = NULL,
  strata.name = NULL,
  scale.model = NULL
)

inla.set.control.hazard.default(...)

Arguments

model

The model for the baseline hazard model. One of 'rw1', 'rw2' or 'iid'. (Default 'rw1'.)
hyper

The definition of the hyperparameters.
fixed

(OBSOLETE!) A boolean variable; is the precision for 'model' fixed? (Default FALSE.)
initial

(OBSOLETE!) The initial value for the precision.
prior

(OBSOLETE!) The prior distribution for the precision for 'model'
param

(OBSOLETE!) The parameters in the prior distribution
constr

A boolean variable; shall the 'model' be constrained to sum to zero?
diagonal

An extra constant added to the diagonal of the precision matrix
n.intervals

Number of intervals in the baseline hazard. (Default 15)
cutpoints

The cutpoints to use. If not specified the they are compute from 'n.intervals' and the maximum length of the interval. (Default NULL)
strata.name

The name of the stratefication variable for the baseline hazard in the data.frame
scale.model

Scale the baseline hazard model (RW1, RW2) so the generalized variance is 1. (Default inla.getOption("scale.model.default").)
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.inla

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.inla(
  strategy = "auto",
  int.strategy = "auto",
  int.design = NULL,
  interpolator = "auto",
  fast = TRUE,
  linear.correction = NULL,
  h = 0.005,
  dz = 0.75,
  diff.logdens = 6,
  print.joint.hyper = TRUE,
  force.diagonal = FALSE,
  skip.configurations = TRUE,
  adjust.weights = TRUE,
  tolerance = 0.005,
  tolerance.f = NULL,
  tolerance.g = NULL,
  tolerance.x = NULL,
  tolerance.step = NULL,
  restart = 0L,
  optimiser = "default",
  verbose = NULL,
  reordering = "auto",
  cpo.diff = NULL,
  npoints = 9,
  cutoff = 1e-04,
  adapt.hessian.mode = NULL,
  adapt.hessian.max.trials = NULL,
  adapt.hessian.scale = NULL,
  adaptive.max = 25L,
  huge = FALSE,
  step.len = 0,
  stencil = 5L,
  lincomb.derived.correlation.matrix = FALSE,
  diagonal = 0,
  numint.maxfeval = 1e+05,
  numint.relerr = 1e-05,
  numint.abserr = 1e-06,
  cmin = -Inf,
  b.strategy = "keep",
  step.factor = -0.1,
  global.node.factor = 2,
  global.node.degree = .Machine$integer.max,
  stupid.search = TRUE,
  stupid.search.max.iter = 1000L,
  stupid.search.factor = 1.05,
  control.vb = INLA::control.vb(),
  num.gradient = "central",
  num.hessian = "central",
  optimise.strategy = "smart",
  use.directions = TRUE,
  constr.marginal.diagonal = sqrt(.Machine$double.eps),
  improved.simplified.laplace = FALSE,
  parallel.linesearch = FALSE,
  compute.initial.values = FALSE,
  hessian.correct.skewness.only = TRUE
)

inla.set.control.inla.default(...)

Arguments

strategy

Character The strategy to use for the approximations; one of 'auto' (default), 'gaussian', 'simplified.laplace', 'laplace' or 'adaptive'.
int.strategy

Character The integration strategy to use; one of 'auto' (default), 'ccd', 'grid', 'eb' (empirical bayes), 'user' or 'user.std'. For the experimental mode, then 'grid' equal 'ccd' for more than two hyperparameters.
int.design

Matrix Matrix of user-defined integration points and weights. Each row consists theta values and the integration weight. (EXPERIMENTAL!).
interpolator

Character The interpolator used to compute the marginals for the hyperparameters. One of 'auto', 'nearest', 'quadratic', 'weighted.distance', 'ccd', 'ccdintegrate', 'gridsum', 'gaussian'. Default is 'auto'.
fast

Logical If TRUE, then replace conditional modes in the Laplace approximation with conditional expectation (default TRUE).
linear.correction

Logical Default TRUE for the 'strategy = laplace' option.
h

Numerical The step-length for the gradient calculations for the hyperparameters. Default 0.005.
dz

Numerical The step-length in the standarised scale for the integration of the hyperparameters. Default 0.75.
diff.logdens

Numerical The difference of the log.density for the hyperpameters to stop numerical integration using int.strategy='grid'. Default 6.
print.joint.hyper

Logical If TRUE, the store also the joint distribution of the hyperparameters (without any costs). Default TRUE.
force.diagonal

Logical If TRUE, then force the Hessian to be diagonal. (Default FALSE)
skip.configurations

Logical Skip configurations if the values at the main axis are to small. (Default TRUE)
adjust.weights

Logical If TRUE then just more accurate integration weights. (Default TRUE.)
tolerance

Numerical The tolerance for the optimisation of the hyperparameters. If set, this is the default value for for '2.5tolerance.f', 'tolerance.g', '5tolerance.x' and '2000*tolerance.step'; see below.
tolerance.f

Numerical The tolerance for the absolute change in the log posterior in the optimisation of the hyperparameters.
tolerance.g

Numerical The tolerance for the absolute change in the gradient of the log posterior in the optimisation of the hyperparameters.
tolerance.x

Numerical The tolerance for the change in the hyperparameters (root-mean-square) in the optimisation of the hyperparameters.
tolerance.step

Numerical The tolerance for the change in root-mean_squre in the inner Newton-like optimisation of the latent field.
restart

Numerical To improve the optimisation, the optimiser is restarted at the found optimum 'restart' number of times.
optimiser

Character The optimiser to use; one of 'gsl' or 'default'.
verbose

Logical Run in verbose mode? (Default FALSE)
reordering

Character Type of reordering to use. (EXPERT OPTION; one of "AUTO", "DEFAULT", "IDENTITY", "REVERSEIDENTITY", "BAND", "METIS", "GENMMD", "AMD", "MD", "MMD", "AMDBAR", "AMDC", "AMDBARC", or the output from inla.qreordering. Default is 'auto'.)
cpo.diff

Numerical Threshold to define when the cpo-calculations are inaccurate. (EXPERT OPTION.)
npoints

Numerical Number of points to use in the 'stratey=laplace' approximation (default 9)
cutoff

Numerical The cutoff used in the 'stratey=laplace' approximation. (Smaller value is more accurate and more slow.) (default 1e-4)
adapt.hessian.mode

Logical Should optimisation be continued if the Hessian estimate is void? (Default TRUE)
adapt.hessian.max.trials

Numerical Number of steps in the adaptive Hessian optimisation
adapt.hessian.scale

Numerical The scaling of the 'h' after each trial.
adaptive.max

Selecting strategy="adaptive" will chose the default strategy for all fixed effects and model components with length less or equal to adaptive.max, for others, the gaussian strategy will be applied.
huge

Logical If TRUE then try to do some of the internal parallelisations differently. Hopefully this will be of benefit for 'HUGE' models. (Default FALSE.) THIS OPTION IS OBSOLETE AND NOT USED!
step.len

Numerical The step-length used to compute numerical derivaties of the log-likelihood (0 means default which depends on stencil)
stencil

Numerical Number of points in the stencil used to compute the numerical derivaties of the log-likelihood (5, 7 or 9). (default 5)
lincomb.derived.correlation.matrix

Logical If TRUE compute also the correlations for the derived linear combinations, if FALSE do not (Default FALSE)
diagonal

Numerical Expert use only! Add a this value on the diagonal of the joint precision matrix. (default 0.0)
numint.maxfeval

Numerical Maximum number of function evaluations in the the numerical integration for the hyperparameters. (Default 100000.)
numint.relerr

Numerical Relative error requirement in the the numerical integration for the hyperparameters. (Default 1e-5)
numint.abserr

Numerical Absolute error requirement in the the numerical integration for the hyperparameters. (Default 1e-6)
cmin

Numerical The minimum value for the negative Hessian from the likelihood. Increasing this value will stabalise the optimisation but can introduce bias. (Default -Inf)
b.strategy

Character If cmin is used, either keep the linear term (with b.strategy="keep") or skip the contribution by setting the linear term to zero (b.strategy="skip"). The default value is "keep"
step.factor

Numerical The step factor in the Newton-Raphson algorithm saying how large step to take (Default 1.0) YES! setting this to a negative values means = 1, EXCEPT the first time (for each thread) where |step.factor| is used.
global.node.factor

Numerical The factor which defines the degree required (how many neighbors), as a fraction of n-1, that is required to be classified as a global node and numbered last (whatever the reordering routine says). Here, n, is the size of the graph. (Disabled if larger than 1, default 2)
global.node.degree

Numerical The degree required (number of neighbors) to be classified as a global node and numbered last (whatever the reordering routine says). (default .Machine$integer.max)
stupid.search

Logical Enable or disable the stupid-search-algorithm, if the Hessian calculations reveals that the mode is not found. (Default TRUE.)
stupid.search.max.iter

Numerical Maximum number of iterations allowed for the stupid-search-algorithm. (default 1000)
stupid.search.factor

Numerical Factor (>=1) to increase the step-length with after each new iteration. (default 1.05)
control.vb

list of arguments for various VB corrections. See control.vb() for details.
num.gradient

Character Set the numerical scheme to compute the gradient, one of "forward" or "central" (default).
num.hessian

Character Set the numerical scheme to compute the Hessian, one of "forward" or "central" (default).
optimise.strategy

Character THIS OPTION IS EXPERIMENTAL. Chose the optimiser strategy, one of "plain" or "smart" (default)
use.directions

THIS OPTION IS EXPERIMENTAL. Unless FALSE or NULL, use directions for computing gradient and Hessian, initialised with use.directions if a matrix.
constr.marginal.diagonal

Add stability to ⁠AQ^-1A^T⁠ by adding a small diagonal term. (default epsilon^0.5)
improved.simplified.laplace

If TRUE use an experimental improved variant, otherwise, use the standard one.
parallel.linesearch

Use serial (default) or parallel line-search (highly experimental for the moment)
compute.initial.values

Compute initial values for the latent field or not. (experimental-mode only)
hessian.correct.skewness.only

If TRUE (default) correct only skewness in the Hessian, for the hyperparameters. If FALSE, correct also variance. (This option is for experimental-mode only)
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.lincomb

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.lincomb(verbose = FALSE)

inla.set.control.lincomb.default(...)

Arguments

verbose

Use verbose mode for linear combinations if verbose model is set globally. (Default FALSE). This option is only available for the default inla.mode (inla.mode="compact").
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.link

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.link(
  model = "default",
  order = NULL,
  variant = NULL,
  hyper = NULL,
  quantile = NULL,
  a = 1,
  initial = NULL,
  fixed = NULL,
  prior = NULL,
  param = NULL
)

inla.set.control.link.default(...)

Arguments

model

The name of the link function/model
order

The order of the link function, where the interpretation of order is model-dependent.
variant

The variant of the link function, where the interpretation of variant is model-dependent.
hyper

Definition of the hyperparameter(s) for the link model chosen
quantile

The quantile for quantile link function
a

The parameter a in the LOGa link
initial

(OBSOLETE!) The initial value(s) for the hyperparameter(s)
fixed

(OBSOLETE!) A boolean variable if hyperparmater(s) is/are fixed or random
prior

(OBSOLETE!) The name of the prior distribution(s) for the hyperparmater(s)
param

(OBSOLETE!) The parameters for the prior distribution(s) for the hyperparmater(s)
...

Named arguments passed on to the main function

Details

The control.link-list is set within the corresponding control.family-list as the link is likelihood-family specific.

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.lp.scale

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.lp.scale(hyper = NULL)

inla.set.control.lp.scale.default(...)

Arguments

hyper

Definition of the hyperparameter(s)
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.mix

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.mix(
  model = NULL,
  hyper = NULL,
  initial = NULL,
  fixed = NULL,
  prior = NULL,
  param = NULL,
  npoints = 101,
  integrator = "default"
)

inla.set.control.mix.default(...)

Arguments

model

The model for the random effect. Currently, only model='gaussian' is implemented
hyper

Definition of the hyperparameter(s) for the random effect model chosen
initial

(OBSOLETE!) The initial value(s) for the hyperparameter(s)
fixed

(OBSOLETE!) A boolean variable if hyperparmater(s) is/are fixed or random
prior

(OBSOLETE!) The name of the prior distribution(s) for the hyperparmater(s)
param

(OBSOLETE!) The parameters for the prior distribution(s) for the hyperparmater(s)
npoints

Number of points used to do the numerical integration (default 101)
integrator

The integration scheme to use (default, quadrature, simpson)
...

Named arguments passed on to the main function

Details

The control.mix list is set within the corresponding control.family-list a the mixture of the likelihood is likelihood specific. (This option is EXPERIMENTAL.)

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.mode

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.mode(
  result = NULL,
  theta = NULL,
  x = NULL,
  restart = TRUE,
  fixed = FALSE
)

inla.set.control.mode.default(...)

Arguments

result

Previous result-object from inla(), a inla-state object or the name of a state-file. Use the theta- and x-mode from this object
theta

The theta-mode/initial values for theta. This option has preference over result$mode$theta.
x

The x-mode/initial values for x. This option has preference over result$mode$x. (This option is less important than theta can often be left out, unless result is used for which it is automatically set.)
restart

A boolean variable; should we restart the optimisation from the given configuration? If TRUE (default), then use this configuration as the initial values (both theta and x) and optimize. If FALSE, then use theta as as the mode and x as the initial value. If x != NULL, theta=NULL and restart=TRUE, then an error will occour unless there are no hyperparameters.
fixed

A boolean variable. If TRUE then treat all theta's as known and fixed, and if FALSE (default) then treat all theta's as unknown and random. If fixed=TRUE and restart=TRUE, then restart is assigned to FALSE and a warning is issued. Note that fixed=TRUE will change the model as the corresponding hyperparmaeters will be defined as fixed.
...

Named arguments passed on to the main function

Details

For internal use only and for algorithms built on to of INLA.

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.numa

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.numa(enable = NULL)

Arguments

enable

Enable NUMA aware cache? (NULL used the value inla.getOption("numa").)

Details

Extra options controlling the NUMA awareness (when NUMA is present)

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.pardiso

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.pardiso(
  verbose = FALSE,
  debug = FALSE,
  parallel.reordering = TRUE,
  nrhs = -1
)

inla.set.control.pardiso.default(...)

Arguments

verbose

Show detailed output (default FALSE)
debug

Show internal debug output (default FALSE)
parallel.reordering

Do reordering in parallel (default TRUE)
nrhs

Number of right-hand sides to solve for in parallel (-1 will determine this adapative)
...

Named arguments passed on to the main function

Details

Extra options controlling the PARDISO library

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.pom

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.pom(cdf = "logit", fast = FALSE)

inla.set.control.pom.default(...)

Arguments

cdf

character The cdf to use, "logit" (default) or "probit"
fast

Logical Use a faster but approximate form for the probit cdf (default FALSE)?
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.predictor

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.predictor(
  hyper = NULL,
  fixed = NULL,
  prior = NULL,
  param = NULL,
  initial = NULL,
  compute = FALSE,
  cdf = NULL,
  quantiles = NULL,
  cross = NULL,
  A = NULL,
  precision = exp(15),
  link = NULL
)

inla.set.control.predictor.default(...)

Arguments

hyper

Definition of the hyperparameters.
fixed

(OBSOLETE!) If the precision for the artificial noise is fixed or not (default TRUE)
prior

(OBSOLETE!) The prior for the artificial noise
param

(OBSOLETE!) Prior parameters for the artificial noise
initial

(OBSOLETE!) The value of the log precision of the artificial noise
compute

A boolean variable; should the marginals for the linear predictor be computed? (Default FALSE.)
cdf

A list of values to compute the CDF for the linear predictor
quantiles

A list of quantiles to compute for the linear predictor
cross

Cross-sum-to-zero constraints with the linear predictor. All linear predictors with the same level of 'cross' are constrained to have sum zero. Use 'NA' for no contribution. 'Cross' has the same length as the linear predictor (including the 'A' matrix extention). (THIS IS AN EXPERIMENTAL OPTION, CHANGES MAY APPEAR.)
A

The observation matrix (matrix or Matrix::sparseMatrix).
precision

The precision for eta* - A*eta, (default exp(15))
link

Define the family-connection for unobserved observations (NA). link is integer values which defines the family connection; family[link[idx]] unless is.na(link[idx]) for which the identity-link is used. The link-argument only influence the fitted.values in the result-object. If is.null(link) (default) then the identity-link is used for all missing observations. If the length of link is 1, then this value is replicated with the length of the response vector. If an element of the response vector is !NA then the corresponding entry in link is not used (but must still be a legal value). Setting this variable implies compute=TRUE.
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.scopy

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.scopy(covariate = NULL, n = 11)

inla.set.control.scopy.default(...)

Arguments

covariate

The covariate for the scopy function
n

Number of locations in the RW2 (n = 2 or 5 <= n <= 15)
...

Named arguments passed on to the main function

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.sem(), control.stiles(), control.taucs(), control.update(), control.vb()

control.sem

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.sem(B = NULL, idx = 0)

inla.set.control.sem.default(...)

Arguments

B

The symbolic B-matrix, where each element is a string giving the expression for that particular element, in terms of beta-parameters for copy
idx

Which diagonal element to use for the variance.
...

Named arguments passed on to the main function

Details

Parameters to family sem

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.stiles(), control.taucs(), control.update(), control.vb()

control.stiles

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.stiles(verbose = FALSE, tile.size = 0)

inla.set.control.stiles.default(...)

Arguments

verbose

Show detailed output (default FALSE)
tile.size

The size of the tile (default 0 will chose automatically)
...

Named arguments passed on to the main function

Details

Extra options controlling the sTiles library

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.taucs(), control.update(), control.vb()

control.taucs

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.taucs(block.size = 40)

inla.set.control.taucs.default(...)

Arguments

block.size

Preferred number of rhs's in each parallel solve
...

Named arguments passed on to the main function

Details

Extra options controlling the TAUCS library

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.update(), control.vb()

control.update

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.update(result = NULL)

inla.set.control.update.default(...)

Arguments

result

Update the joint posterior for the hyperparameters from result
...

Named arguments passed on to the main function

See Also

inla()

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.vb()

control.fixed

Description

Control variables in ⁠control.*⁠ for use with inla(). The functions can be used to TAB-complete arguments, and returns a list of the default control arguments, unless overridden by specific input arguments.

Usage

control.vb(
  enable = "auto",
  strategy = c("mean", "variance"),
  verbose = TRUE,
  iter.max = 25,
  emergency = 25,
  f.enable.limit = c(30, 25, 1024, 768),
  hessian.update = 2,
  hessian.strategy = c("default", "full", "partial", "diagonal")
)

inla.set.control.vb.default(...)

Arguments

enable

Logical/Character Use this feature? If "auto" this will be selected automatically.
strategy

Character What to correct, either "mean" or "variance".
verbose

Logical Be verbose or not.
iter.max

Integer Maximum number of iterations.
emergency

Numeric If the standardized correction for the mean is larger than this value, then call the vb.correction off and issue a warning
f.enable.limit

Vector of length 4. The size limit to correct for a f(). First element is for strategy="mean". Second element is for strategy="variance". Third element is overall maximum dimension of the correction for strategy="mean". Forth element is overall maximum dimension of the correction for strategy="variance".
hessian.update

How many times the Hessian is updated for each correction.
hessian.strategy

Select strategy for computing the Hessian matrix for strategy="variance", one of "full", "diagonal", "partial" and "default".
...

Named arguments passed on to the main function

Details

control.vb List of arguments for various VB corrections. Used for control.inla control.vb specifications.

See Also

Other control: control.bgev(), control.compute(), control.expert(), control.family(), control.fixed(), control.gcpo(), control.group(), control.hazard(), control.inla(), control.lincomb(), control.link(), control.lp.scale(), control.mix(), control.mode(), control.numa(), control.pardiso(), control.pom(), control.predictor(), control.scopy(), control.sem(), control.stiles(), control.taucs(), control.update()

Handling CRS/WKT

Description

[Deprecated] in favour of fmesher::fm_wkt() and related methods.

Get and set CRS object or WKT string properties.

Usage

inla.wkt_is_geocent(wkt)

inla.crs_is_geocent(crs)

inla.wkt_get_ellipsoid_radius(wkt)

inla.crs_get_ellipsoid_radius(crs)

inla.wkt_set_ellipsoid_radius(wkt, radius)

inla.crs_set_ellipsoid_radius(crs, radius)

inla.wkt_unit_params()

inla.wkt_get_lengthunit(wkt)

inla.wkt_set_lengthunit(wkt, unit, params = NULL)

inla.crs_get_wkt(crs)

inla.crs_get_lengthunit(crs)

inla.crs_set_lengthunit(crs, unit, params = NULL)

Arguments

wkt

A WKT2 character string
crs

A sp::CRS or inla.CRS object
radius

numeric
unit

character, name of a unit. Supported names are "metre", "kilometre", and the aliases "meter", "m", International metre", "kilometer", and "km", as defined by inla.wkt_unit_params or the params argument. (For legacy PROJ4 use, only "m" and "km" are supported)
params

Length unit definitions, in the list format produced by inla.wkt_unit_params(), Default: NULL, which invokes inla.wkt_unit_params()

Value

For inla.wkt_unit_params, a list of named unit definitions

For inla.wkt_get_lengthunit, a list of length units used in the wkt string, excluding the ellipsoid radius unit.

For inla.wkt_set_lengthunit, a WKT2 string with altered length units. Note that the length unit for the ellipsoid radius is unchanged.

For inla.crs_get_wkt, WKT2 string.

For inla.crs_get_lengthunit, a list of length units used in the wkt string, excluding the ellipsoid radius unit. (For legacy PROJ4 code, the raw units from the proj4string are returned, if present.)

For inla.crs_set_lengthunit, a sp::CRS object with altered length units. Note that the length unit for the ellipsoid radius is unchanged.

For inla.wkt_unit_params, a list of named unit definitions

For inla.wkt_get_lengthunit, a list of length units used in the wkt string, excluding the ellipsoid radius unit.

For inla.wkt_set_lengthunit, a WKT2 string with altered length units. Note that the length unit for the ellipsoid radius is unchanged.

For inla.crs_get_wkt, WKT2 string.

For inla.crs_get_lengthunit, a list of length units used in the wkt string, excluding the ellipsoid radius unit. (For legacy PROJ4 code, the raw units from the proj4string are returned, if present.)

For inla.crs_set_lengthunit, a sp::CRS object with altered length units. Note that the length unit for the ellipsoid radius is unchanged.

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.sp_get_crs()

inla.sp_get_crs()

Examples

## Not run: 
c1 <- fmesher::fm_crs("globe")
inla.crs_get_lengthunit(c1)
c2 <- inla.crs_set_lengthunit(c1, "metre")
inla.crs_get_lengthunit(c2)

## End(Not run)

## Not run: 
c1 <- inla.CRS("globe")
inla.crs_get_lengthunit(c1)
c2 <- inla.crs_set_lengthunit(c1, "km")
inla.crs_get_lengthunit(c2)

## End(Not run)

Group-wise model criticism using node-splitting

Description

This function performs group-wise, cross-validatory model assessment for an INLA model using so-called node-splitting (Marshall and Spiegelhalter, 2007; Presanis et al, 2013). The user inputs an object of class inla (i.e. a result of a call to inla()) as well as a variable name (split.by) specifying a grouping: Data points that share the same value of split.by are in the same group. The function then checks whether each group is an "outlier", or in conflict with the remaining groups, using the methodology described in Ferkingstad et al (2017). The result is a vector containing a p-value for each group, corresponding to a test for each group i, where the null hypothesis is that group i is consistent with the other groups except i (so a small p-value is evidence that the group is an "outlier"). See Ferkingstad et al (2017) for further details.

Usage

inla.cut(result, split.by, mc.cores = NULL, debug = FALSE)

Arguments

result

An object of class inla, i.e. a result of a call to inla()
split.by

The name of the variable to group by. Data points that have the same value of split.by are in the same group.
mc.cores

The number of cores to use in parallel::mclapply. If is.null(mc.cores), then check getOption("mc.cores") and inla.getOption("num.threads") in that order.
debug

Print debugging information if TRUE, default is FALSE

Value

A numeric vector of p-values, corresponding to a test for each group i where the null hypothesis is that group i is consistent with the other groups except i. A small p-value for a group indicates that the group is an "outlier" (in conflict with remaining groups).

This function is EXPERIMENTAL!!!

Author(s)

Egil Ferkingstad egil.ferkingstad@gmail.com and Havard Rue hrue@r-inla.org

References

Ferkingstad, E., Held, L. and Rue, H. (2017). Fast and accurate Bayesian model criticism and conflict diagnostics using R-INLA. arXiv preprint arXiv:1708.03272, available at http://arxiv.org/abs/1708.03272. Published in Stat, 6:331-344 (2017).

Marshall, E. C. and Spiegelhalter, D. J. (2007). Identifying outliers in Bayesian hierarchical models: a simulation-based approach. Bayesian Analysis, 2(2):409-444.

Presanis, A. M., Ohlssen, D., Spiegelhalter, D. J., De Angelis, D., et al. (2013). Conflict diagnostics in directed acyclic graphs, with applications in Bayesian evidence synthesis. Statistical Science, 28(3):376-397.

Examples

## See http://www.r-inla.org/examples/case-studies/ferkingstad-2017 and Ferkingstad et al (2017).

Debug a graph-file

Description

Debug a graph specification on file (ascii-mode only), by checking the specification along the way.

Usage

inla.debug.graph(graph.file)

Arguments

graph.file

The filename of the graph (ascii-mode)

Value

If an error is found, then an error message is shows, otherwise the graph-object returned by inla.read.graph() is returned.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.read.graph

Examples

## Not run: 
cat("3\n 1 1 2n\ 2 1 1\n 3 4\n", file="g.dat")
g = inla.debug.graph("g.dat")

## End(Not run)

Time series with seasonal effect

Description

Montly total of car drivers killed or several injuried in England from January 1969 to December 1984

Format

A data frame with 204 observations on the following 4 variables.

y

Number of deaths

belt

Indicator of weather the belt was compulsory to use (1) or not (0)

trend

time (in months)

seasonal

time (in months)

Details

NB: The last 12 lines of the data set have the first column set to NULL since these data where not observed but we want to predict them.

References

Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall

Examples

data(Drivers)

Do a dryrun to extract some internal information upfront

Description

Do a dryrun to get information about the internal storage and the list (and ordering) of the hyperparameters

Usage

inla.dryrun(...)

Arguments

...

Same arguments as inla()

Value

A list of start-index and length for each latent component and a list of the hyperparameters in the model

Author(s)

Havard Rue hrue@r-inla.org

Repeated measures on Poisson counts

Description

Seizure counts in a randomised trial of anti-convulsant therpay in epilepsy for 59 patients.

Format

A data frame with 236 observations on the following 7 variables.

y

Number of seizures

Trt

indicator for the presence of treatment

Base

8-week baseline seizure counts

Age

Age of the patient

V4

indicator variable for the 4th visit.

rand

a numeric vector

Ind

indicator for the specific patient

Source

WinBUGS/OpenBUGS Manual Examples Vol I

Examples

data(Epil)

Extract tagged boundary/internal segments.

Description

[Deprecated] Use fmesher::fm_segm() instead.

Extract boundary or internal segments tagged by group id:s.

Usage

extract.groups(segm, groups, groups.new = groups, ...)

Arguments

segm

An inla.mesh.segment() object.
groups

The segment groups id:s to extract.
groups.new

Optional vector of group id remapping; groups[k] in the input will be replaced by groups.new[k] in the output.
...

Additional arguments, passed on to other methods.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.segment()

Define general Gaussian models in the INLA formula

Description

Function used for defining of smooth and spatial terms within inla model formulae. The function does not evaluate anything - it exists purely to help set up a model. The function specifies one smooth function in the linear predictor (see inla.list.models()) as

w f(x)w\ f(x)

Usage

f(
  ...,
  model = "iid",
  copy = NULL,
  scopy = NULL,
  same.as = NULL,
  n = NULL,
  nrep = NULL,
  replicate = NULL,
  ngroup = NULL,
  group = NULL,
  control.group = inla.set.control.group.default(),
  control.scopy = inla.set.control.scopy.default(),
  hyper = NULL,
  initial = NULL,
  prior = NULL,
  param = NULL,
  fixed = NULL,
  season.length = NULL,
  constr = NULL,
  extraconstr = list(A = NULL, e = NULL),
  values = NULL,
  cyclic = NULL,
  diagonal = NULL,
  graph = NULL,
  graph.file = NULL,
  cdf = NULL,
  quantiles = NULL,
  Cmatrix = NULL,
  rankdef = NULL,
  Z = NULL,
  nrow = NULL,
  ncol = NULL,
  nu = NULL,
  bvalue = NULL,
  spde.prefix = NULL,
  spde2.prefix = NULL,
  spde2.transform = c("logit", "log", "identity"),
  spde3.prefix = NULL,
  spde3.transform = c("logit", "log", "identity"),
  mean.linear = inla.set.control.fixed.default()$mean,
  prec.linear = inla.set.control.fixed.default()$prec,
  compute = TRUE,
  of = NULL,
  precision = 10^8,
  range = NULL,
  adjust.for.con.comp = TRUE,
  order = NULL,
  scale = NULL,
  rgeneric = NULL,
  cgeneric = NULL,
  scale.model = NULL,
  args.slm = list(rho.min = NULL, rho.max = NULL, X = NULL, W = NULL, Q.beta = NULL),
  args.ar1c = list(Z = NULL, Q.beta = NULL),
  args.intslope = list(subject = NULL, strata = NULL, covariates = NULL),
  vb.correct = TRUE,
  locations = NULL,
  debug = FALSE,
  A.local = NULL
)

Arguments

...

Name of the covariate and, possibly of the weights vector. NB: order counts!!!! The first specified term is the covariate and the second one is the vector of weights (which can be negative).
model

A string indicating the chosen model. The default is iid. See names(inla.models()$latent) for a list of possible alternatives and inla.doc() for detailed docs.
copy

The name of the model-component to copy
scopy

The name of the model-component to smooth-copy (where the copy-function is a spline)
same.as

Can be used with copy="..". same.as="A" says that this copy should use the same scaling parameter as another copy "A"
n

An optional argument which defines the dimension of the model if this is different from length(sort(unique(covariate)))
nrep

Number of replications, if not given, then nrep=max(replications)
replicate

A vector of which replications to use.
ngroup

Number of groups, if not given, then ngroup=max(group)
group

A vector of which groups to use.
control.group

Controls the use of group
control.scopy

Controls the use of scopy
hyper

Specification of the hyperparameter, fixed or random, initial values, priors and its parameters. See ?inla.models for the list of hyparameters for each model and its default options or use inla.doc() for detailed info on the family and supported prior distributions.
initial

THIS OPTION IS OBSOLETE, DO NOT USE
prior

THIS OPTION IS OBSOLETE, DO NOT USE
param

THIS OPTION IS OBSOLETE, DO NOT USE
fixed

THIS OPTION IS OBSOLETE; DO NOT USE
season.length

Length of the seasonal component for model="seasonal"
constr

A boolean variable indicating whater to set a sum to 0 constraint on the term. By default the sum to 0 constraint is imposed on all intrinsic models ("iid","rw1","rw1","besag", etc..).
extraconstr

This argument defines extra linear constraints. The argument is a list with two elements, a matrix A and a vector e, which defines the extra constraint Ax = e; for example extraconstr = list(A = A, e=e). The number of columns of A must correspond to the length of this f-model. Note that this constraint comes additional to the sum-to-zero constraint defined if constr = TRUE.
values

An optional vector giving all values assumed by the covariate for which we want estimated the effect. It must be a numeric vector, a vector of factors or NULL.
cyclic

A boolean specifying wheather the model is cyclical. Only valid for "rw1" and "rw2" models, is cyclic=T then the sum to 0 constraint is removed. For the correct form of the grah file see Martino and Rue (2008).
diagonal

An extra constant added to the diagonal of the precision matrix to prevent numerical issues.
graph

Defines the graph-object either as a file with a graph-description, an inla.graph-object, or as a (sparse) symmetric matrix .
graph.file

THIS OPTION IS OBSOLETE, DO NOT USE
cdf

THIS OPTION IS OBSOLETE, DO NOT USE
quantiles

A vector of maximum 10 quantiles, p(0),p(1),…p(0), p(1),\dots to compute for each posterior marginal. The function returns, for each posterior marginal, the values x(0),x(1),…x(0), x(1),\dots such that

Prob(X<x(p))=p\mbox{Prob}(X<x(p))=p

Cmatrix

The specification of the precision matrix for the generic, generic3 or z models (up to a scaling constant). Cmatrix is either a (dense) matrix, a matrix created using Matrix::sparseMatrix(), or a filename which stores the non-zero elements of Cmatrix, in three columns: i, j and Qij. In case of the generic3 model, it is a list of such specifications.
rankdef

A number defining the rank deficiency of the model, with sum-to-zero constraint and possible extra-constraints taken into account. See details.
Z

The matrix for the z-model
nrow

Number of rows for 2d-models
ncol

Number of columns for 2d-models
nu

Smoothing parameter for the Matern2d-model, possible values are c(0, 1, 2, 3)
bvalue

The boundary conditions for model rw2d, 0 means use the correct subspace (default), while 1 means condition on 0's outside
spde.prefix

Internal use only
spde2.prefix

Internal use only
spde2.transform

Internal use only
spde3.prefix

Internal use only
spde3.transform

Internal use only
mean.linear

Prior mean for model="linear"
prec.linear

Prior precision for model="linear"
compute

A boolean variable indicating whether the marginal posterior distribution for the nodes in the f() model should be computed or not. This is usefull for large models where we are only interested in some posterior marginals.
of

Internal use only
precision

The precision for the artificial noise added when creating a copy of a model and others.
range

A vector of size two giving the lower and upper range for the scaling parameter beta in the model COPY, CLINEAR, MEC and MEB. If low = high then the identity mapping is used.
adjust.for.con.comp

If TRUE (default), adjust some of the models (currently: besag, bym, bym2 and besag2) if the number of connected components in graph is larger than 1. If FALSE, do nothing.
order

Defines the order of the model: for model ar this defines the order p, in AR(p). Not used for other models at the time being.
scale

A scaling vector. Its meaning depends on the model.
rgeneric

A object of class inla.rgeneric which defines the model. (EXPERIMENTAL!)
cgeneric

A object of class inla.cgeneric which defines the model. (EXPERIMENTAL!)
scale.model

Logical. If TRUE then scale the RW1 and RW2 and BESAG and BYM and BESAG2 and RW2D models so the their (generlized) variance is 1. Default value is inla.getOption("scale.model.default")
args.slm

Required arguments to the model="slm"; see the documentation for further details.
args.ar1c

Required arguments to the model="ar1c"; see the documentation for further details.
args.intslope

A list with the subject (factor), strata (factor) and covariates (numeric) for the intslope model; see the documentation for further details,
vb.correct

Add this model component to the list of nodes to be used for the (potential) vb correction? If TRUE do, and do not if FALSE. Can also be a vector of nodes to add in the correction-set.
locations

A matrix with locations for the model dmatern. This also defines n.
debug

Enable local debug output
A.local

Local A-matrix (experimental and in development, do not use)

Details

There is no default value for rankdef, if it is not defined by the user then it is computed by the rank deficiency of the prior model (for the generic model, the default is zero), plus 1 for the sum-to-zero constraint if the prior model is proper, plus the number of extra constraints. Oops: This can be wrong, and then the user must define the rankdef explicitly.

Value

TODO

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla(), hyperpar.inla()

Return the coefficients in the 3-component AR(1) mixture representing FGN(H)

Description

This function will return the coefficients in the 3-component AR(1) mixture representing FGN(H)

Usage

inla.fgn(H, K = 4L, lag.max = NULL, approx = TRUE)

Arguments

H

The Hurst coeffcient (0<H<1), or a vector of those
K

The number of components in representation, must be 3L or 4L
lag.max

Integer. If positive integer, return the coeffcients implicitely as the ACF from 0 to lag.max
approx

Logical. If lag.max is an positive integer and approx is FALSE, then return the true ACF instead of the approximated one.

Value

inla.fgn returns a named matrix. If is.null(lag.max), then first column is H, columns 1+1:K are lag one correlations (or phi's), and columns 1+K+1:K are the weights. If lag.max > 0, then return the ACFs in columns 2+(0:lag.max), for the H in column 1, either the approximated ones or the the true ones.

This function is EXPERIMENTAL!!!

Author(s)

Havard Rue hrue@r-inla.org

Examples

r = c(inla.fgn(0.7))
     r_m = inla.fgn(seq(0.6, 0.8, by=0.01))

Disease Mapping

Description

Cases of Oral cavity cancer in Germany from 1986-1990

Format

A data frame with 544 observations on the following 4 variables.

region

Region of Germany

E

Fixed quantity which accounts for number of people in the district (offset)

Y

Number of cases

x

covariate measuring smoking consumption

References

Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall

Examples

data(Germany)

INLA utility functions

Description

Various utility functions for INLA

Usage

inla.geobugs2inla(adj, num, graph.file = "graph.dat")

Arguments

adj

A vector listing the ID numbers of the adjacent areas for each area. This is a sparse representation of the full adjacency matrix for the study region, and can be generated using the Adjacency Tool from the Map menu in GeoBUGS.
num

A vector of length N (the total number of areas) giving the number of neighbours n.i for each area.
graph.file

Name of the file of the new graph in the INLA format.

Value

The return value is the name of the graph-file created.

Note

These are all the same function, and the two different names are due to backward-compatibility

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla(), inla.surv(), hyperpar.inla()

Construct a neighbour-matrix from a graph

Description

Construct a neighbour-matrix from a graph and disaply it

Usage

inla.matrix2graph(graph, ...)

inla.graph2matrix(graph, ...)

inla.spy(graph, ..., reordering = NULL)

Arguments

graph

An inla.graph-object, a (sparse) symmetric matrix, a filename containing the graph, or a list or collection of characters and/or numbers defining the graph.
...

Additional arguments to inla.read.graph()
reordering

A possible reordering. Typical the one obtained from a inla-call, result$misc$reordering, or the result of inla.qreordering.

Value

inla.graph2matrix returns a sparse symmetric matrix where the non-zero pattern is defined by the graph. The inla.spy function, plots the associated sparse matrix defined by the graph. The reordering argument is typically the reordering found by inla.qreordering().

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.read.graph(), inla.qreordering()

Examples

n = 50
Q = matrix(0, n, n)
idx = sample(1:n, 2*n, replace=TRUE)
Q[idx, idx] = 1
diag(Q) = 1
g = inla.read.graph(Q)
QQ = inla.graph2matrix(g)
inla.spy(QQ)
print(all.equal(as.matrix(Q), as.matrix(QQ)))

g.file = inla.write.graph(g, filename = tempfile())
inla.dev.new()
inla.spy(g.file)
inla.spy(g.file,  reordering = inla.qreordering(g))

g = inla.read.graph(g.file)
inla.dev.new()
inla.spy(g)

## Old examples that don't work with the inla.spy call syntax:
# inla.dev.new()
# inla.spy(3, 1, "1 2 2 1 1 3 0")
# inla.dev.new()
# inla.spy(3, 1, "1 2 2 1 1 3 0", reordering = 3:1)

Convert indexes

Description

Convert indexes given by to triplet ‘(idx, group, replicate)’ to the (one-dimensional) index used in the grouped and replicated model

Usage

inla.idx(
  idx,
  n = max(idx),
  group = rep(1, length(idx)),
  ngroup = max(group),
  replicate = rep(1, length(idx)),
  nrep = max(replicate)
)

Arguments

idx

The index within the basic model. (Legal values from ⁠1' to ⁠n'.)
n

The length ‘n’ of the basic model.
group

The index within group. (Legal values from ⁠1' to ⁠ngroup'.)
ngroup

Number of groups.
replicate

The index within replication. (Legal values from ⁠1' to ⁠nrep'.)
nrep

Number of replications.

Value

inla.idx returns indexes in the range ⁠1' to ⁠nngroupnrep' representing where the triplet ‘(idx,group,replicate)’ is stored internally in the full grouped and replicated model.

Author(s)

Havard Rue hrue@r-inla.org

Examples

##TODO

Bayesian analysis of structured additive models

Description

inla performs a full Bayesian analysis of additive models using Integrated Nested Laplace approximation

Usage

inla(
  formula = NULL,
  family = "gaussian",
  contrasts = NULL,
  data = NULL,
  quantiles = c(0.025, 0.5, 0.975),
  E = NULL,
  offset = NULL,
  scale = NULL,
  weights = NULL,
  Ntrials = NULL,
  strata = NULL,
  lp.scale = NULL,
  link.covariates = NULL,
  verbose = inla.getOption("verbose"),
  lincomb = NULL,
  selection = NULL,
  control.compute = list(),
  control.predictor = list(),
  control.family = list(),
  control.inla = list(),
  control.fixed = list(),
  control.mode = list(),
  control.expert = list(),
  control.hazard = list(),
  control.lincomb = list(),
  control.update = list(),
  control.lp.scale = list(),
  control.pardiso = list(),
  control.stiles = list(),
  control.taucs = list(),
  control.numa = list(),
  only.hyperparam = FALSE,
  inla.call = inla.getOption("inla.call"),
  inla.arg = inla.getOption("inla.arg"),
  num.threads = inla.getOption("num.threads"),
  keep = inla.getOption("keep"),
  working.directory = inla.getOption("working.directory"),
  silent = inla.getOption("silent"),
  inla.mode = inla.getOption("inla.mode"),
  safe = inla.getOption("safe"),
  debug = inla.getOption("debug"),
  .parent.frame = environment(formula)
)

Arguments

formula

A inla formula like y ~1 + z + f(ind, model="iid") + f(ind2, weights, model="ar1") This is much like the formula for a glm except that smooth or spatial terms can be added to the right hand side of the formula. See f() for full details and the web site www.r-inla.org for several worked out examples. Each smooth or spatial term specified through f should correspond to separate column of the data frame data. The response variable, y can be a univariate response variable, a list or the output of the function inla.surv() for survival analysis models.
family

A string indicating the likelihood family. The default is gaussian with identity link. See names(inla.models()$likelihood) for a list of possible alternatives and use inla.doc() for detailed docs for individual families.
contrasts

Optional contrasts for the fixed effects; see ?lm or ?glm for details.
data

A data frame or list containing the variables in the model. The data frame MUST be provided
quantiles

A vector of quantiles, p(0),p(1),…p(0), p(1),\dots to compute for each posterior marginal. The function returns, for each posterior marginal, the values x(0),x(1),…x(0), x(1),\dots such that

Prob(X<x(p))=p\mbox{Prob}(X<x(p))=p

E

Known component in the mean for the Poisson likelihoods defined as

Eiexp⁡(ηi)E_i\exp(\eta_i)

where

ηi\eta_i

is the linear predictor. If not provided it is set to rep(1, n.data).
offset

This argument is used to specify an a-priori known and fixed component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length either one or equal to the number of cases. One or more offset() terms can be included in the formula instead or as well, and if both are used, they are combined into a common offset. If the A-matrix is used in the linear predictor statement control.predictor, then the offset given in this argument is added to ⁠eta*⁠, the linear predictor related to the observations, as ⁠eta* = A eta + offset⁠, whereas an offset in the formula is added to eta, the linear predictor related to the formula, as eta = ... + offset.formula. So in this case, the offset defined here and in the formula has a different meaning and usage.
scale

Fixed (optional) scale parameters of the precision for Gaussian and Student-T response models. Default value is rep(1, n.data).
weights

Fixed (optional) weights parameters of the likelihood, so the log-likelihood[i] is changed into weights[i]*log-likelihood[i]. Default value is rep(1, n.data). WARNING: The normalizing constant for the likelihood is NOT recomputed, so ALL marginals (and the marginal likelihood) must be interpreted with great care.
Ntrials

A vector containing the number of trials for the binomial likelihood and variantes, or the number of required successes for the nbinomial2 likelihood. Default value is rep(1, n.data).
strata

Fixed (optional) strata indicators for tstrata likelihood model and similar. The documentation for each likelihood will inform if this argument is required.
lp.scale

A vector with same length as the predictor going into the likelihood with either NA's or indices indexing the scaling coefficients. NA or a index less or equal to 0 means no scaling. The priors and properties of the scaling coefficients are set in control.lp.scale. Note that only the non-offset part of the linear predictor is scaled. This is an EXPERIMENTAL option.
link.covariates

A vector or matrix with covariates for link functions
verbose

Boolean indicating if the inla-program should run in a verbose mode (default inla.getOption("verbose"))
lincomb

Used to define linear combination of nodes in the latent field. The posterior distribution of such linear combination is computed by the inla function. See vignette ⁠Short tutorials from old www-page⁠ for information on how to define such linear combinations.
selection

This is a similar argument to the one in inla.posterior.sample and follow the same format. This argument allows to define a subset of the latent field for which to compute an approximated joint distribution. It will appear in result$selection. See also ?inla.rjmarginal and the approriate vignette.
control.compute

See ?control.compute
control.predictor

See ?control.predictor
control.family

See ?control.family
control.inla

See ?control.inla
control.fixed

See ?control.fixed
control.mode

See ?control.mode
control.expert

See ?control.expert
control.hazard

See ?control.hazard
control.lincomb

See ?control.lincomb
control.update

See ?control.update
control.lp.scale

See ?control.lp.scale
control.pardiso

See ?control.pardiso
control.stiles

See ?control.stiles
control.taucs

See ?control.taucs
control.numa

See ?control.numa
only.hyperparam

If TRUE, then only the hyperparameters are computed.
inla.call

The path to, or the name of, the inla-program. This is program is installed together with the R-package, but, for example, a native compiled version can be used instead to improve the performance.
inla.arg

A string indicating ALL arguments to the 'inla' program and do not include default arguments. (This is an expert option and not intended for normal usage.)
num.threads

Maximum number of threads the inla-program will use, or as 'A:B' defining the number threads in the outer (A) and inner (B) layer for nested parallelism. If B is set to -1, then one can force some single function evaluations to be perfored in parallel, so num.threads=4:-1 will locally behave like num.threads=4:1 (if considered to be more efficient). If B > 1 then num.threads=A:B and num.threads=A:-B are equivalent.
keep

A boolean variable indicating that the working files (ini file, data files and results files) should be kept. If TRUE and no working.directory is specified, the model-files are stored in the current directory called "inla.model" or "inla.model-NUMBER".
working.directory

A string giving the name of an non-existing directory where to store the model-files. Sometimes this argument is required if the temporary directory returned with tempdir() not writeable or has an encoding that is not supported.
silent

If equal to 1L or TRUE, then the inla-program would be “silent”. If equal to 2L, then supress also error messages from the inla-program.
inla.mode

Run inla in compact-mode, or the classic-mode. Default is to use the mode set by inla.getOption("inla.mode") which is default compact-mode.
safe

If TRUE, then enable possible restarts to improve initial values and Hessian if needed.
debug

If TRUE, print some debug output.
.parent.frame

Internal use only

Value

inla returns an object of class "inla". This is a list containing at least the following arguments:

summary.fixed

Matrix containing the mean and standard deviation (plus, possibly quantiles and cdf) of the the fixed effects of the model.
marginals.fixed

A list containing the posterior marginal densities of the fixed effects of the model.
summary.random

List of matrices containing the mean and standard deviation (plus, possibly quantiles and cdf) of the the smooth or spatial effects defined through f().
marginals.random

A list containing the posterior marginal densities of the random effects defined through f.
summary.hyperpar

A matrix containing the mean and sd (plus, possibly quantiles and cdf) of the hyperparameters of the model

marginals.hyperpar

A list containing the posterior marginal densities of the hyperparameters of the model.
summary.linear.predictor

A matrix containing the mean and sd (plus, possibly quantiles and cdf) of the linear predictors η\eta in the model

marginals.linear.predictor

If compute=TRUE in control.predictor, a list containing the posterior marginals of the linear predictors η\eta in the model.

summary.fitted.values

A matrix containing the mean and sd (plus, possibly quantiles and cdf) of the fitted values g−1(η)g^{-1}(\eta) obtained by transforming the linear predictors by the inverse of the link function. This quantity is only computed if marginals.fitted.values is computed. Note that if an observation is NA then the identity link is used. You can manually transform a marginal using inla.marginal.transform() or set the argument link in the control.predictor-list; see ?control.predictor

marginals.fitted.values

If compute=TRUE in control.predictor, a list containing the posterior marginals of the fitted values g−1(η)g^{-1}(\eta) obtained by transforming the linear predictors by the inverse of the link function. Note that if an observation is NA then the identity link is used. You can manually transform a marginal using inla.marginal.transform() or set the argument link in the control.predictor-list; see ?control.predictor

summary.lincomb

If lincomb != NULL a list of matrices containing the mean and sd (plus, possibly quantiles and cdf) of all linear combinations defined.

marginals.lincomb

If lincomb != NULL a list of posterior marginals of all linear combinations defined.

selection

Provide the approximated joint distribution for the selection
dic

If dic=TRUE in control.compute, the deviance information criteria and effective number of parameters, otherwise NULL

cpo

If cpo=TRUE in control.compute, a list of three elements: cpo$cpo are the values of the conditional predictive ordinate (CPO), cpo$pit are the values of the probability integral transform (PIT) and cpo$failure indicates whether some assumptions are violated. In short, if cpo$failure[i] > 0 then some assumption is violated, the higher the value (maximum 1) the more seriously.

po

If po=TRUE in control.compute, a list of one elements: po$po are the values of the predictive ordinate (CPO) (pi(yi|y))

residuals

If residuals=TRUE in control.compute, a list of standardized residuals are provided, see ?control.compute for details

waic

If waic=TRUE in control.compute, a list of two elements: waic$waic is the Watanabe-Akaike information criteria, and waic$p.eff is the estimated effective number of parameters

mlik

If mlik=TRUE in control.compute, the log marginal likelihood of the model (using two different estimates), otherwise NULL

neffp

Expected effective number of parameters in the model. The standard deviation of the expected number of parameters and the number of replicas for parameter are also returned
mode

A list of two elements: mode$theta is the computed mode of the hyperparameters and mode$x is the mode of the latent field given the modal value of the hyperparameters.

call

The matched call.
formula

The formula supplied
nhyper

The number of hyperparameters in the model
cpu.used

The cpu time used by the inla function

Author(s)

Havard Rue hrue@r-inla.org and Sara Martino

See Also

f()

inla estimation object class

Description

The inla class is defined in the INLA package

See Also

inla

Aggregate Gaussian into an equivalent observation

Description

Aggregate Gaussians observed with the same mean and precision, into an equivalent triplet, for use with family="agaussian"

Usage

inla.agaussian(y, s = NULL)

Arguments

y

Repeated observations. If y is a matrix, then each row represents repeated observations. if y is a list, then each element of the list is a vector of repeated observations. If y is a vector, then the whole vector represents repeated observations. The optional scaling s, must have the same format as y, ie matrix or vector. NA's in y (and s) are removed and not used or counted. If s is given, then the NA-pattern in y and s must be the same.
s

Optional fixed scaling of the precisions. Must be in the same format as y, and have the same NA-pattern. See the documentation for details.

Value

The output is a inla.mdata-object ready for use with family="agaussian". See the example in the documentation.

Author(s)

Havard Rue hrue@r-inla.org

Examples

A = matrix(1:25,5,5)
 inla.agaussian(A)

 A[1,-1] = NA
 A[2,-(2:3)] = NA
 inla.agaussian(A)

Convert between parameterizations for the AR(p) model

Description

These functions convert between the AR(p) coefficients phi, the partial autorcorrelation coefficients pacf and the autocorrelation function acf. The phi-parameterization is the same as used for arima-models in R; see ?arima and the parameter-vector a in Details.

Usage

inla.ar.pacf2phi(pac)

inla.ar.phi2pacf(phi)

inla.ar.phi2acf(phi, lag.max = length(phi))

inla.ar.pacf2acf(pac, lag.max = length(pac))

Arguments

pac

The partial autorcorrelation coefficients
phi

The AR(p) parameters phi
lag.max

The maximum lag to compute the ACF for

Value

  • inla.ar.pacf2phi returns phi for given pacf.

  • inla.ar.phi2pacf returns pac for given phi.

  • inla.ar.phi2acf returns acf for given phi.

  • inla.ar.pacf2acf returns acf for given pacf.

Author(s)

Havard Rue hrue@r-inla.org

Examples

pac <- runif(5)
phi <- inla.ar.pacf2phi(pac)
pac2 <- inla.ar.phi2pacf(phi)
print(paste("Error:", max(abs(pac2 - pac))))
print("Correlation matrix (from pac)")
print(toeplitz(inla.ar.pacf2acf(pac)))
print("Correlation matrix (from phi)")
print(toeplitz(inla.ar.phi2acf(phi)))

Convert a matrix or sparse matrix into the sparse formate used by INLA

Description

Convert a matrix or sparse matrix into the sparse format used by INLA (dgTMatrix)

Usage

inla.as.sparse(A, unique = TRUE, na.rm = FALSE, zeros.rm = FALSE)

inla.as.dgTMatrix(...)

Arguments

A

The matrix
unique

Logical. If TRUE, then ensure that the internal representation is unique and there are no duplicated entries. (Do not change this unless you know what you are doing.)
na.rm

Replace NA's in the matrix with zeros.
zeros.rm

Remove zeros in the matrix.
...

The arguments. The matrix or sparse matrix, and the additonal arguments

Value

inla.as.sparse and inla.as.dgTMatrix is the same function. The returned value is a sparse matrix in the dgTMatrix-format.

Author(s)

Havard Rue hrue@r-inla.org

Examples

A = matrix(1:9, 3, 3)
 inla.as.sparse(A)

Internal WKT handling

Description

[Deprecated] in favour of fmesher::fm_wkt_as_wkt_tree().

Conversion between WKT and a tree representation

Usage

inla.as.wkt_tree.wkt(x, ...)

inla.as.wkt.wkt_tree(x, pretty = FALSE, ...)

inla.wkt_tree_get_item(x, item, duplicate = 1)

inla.wkt_tree_set_item(x, item_tree, duplicate = 1)

Arguments

x

A WKT2 string, or a wkt_tree list structure
...

Unused
pretty

logical
item

character vector with item labels identifying a parameter item entry.
duplicate

For items that have more than one match, duplicate indicates the index number of the desired version. Default: 1
item_tree

An item tree identifying a parameter item entry

Functions for defining the Barrier models

Description

Functions for defining Barrier models as an ⁠inla rgeneric⁠ model

Usage

inla.barrier.pcmatern(
  mesh,
  barrier.triangles,
  prior.range,
  prior.sigma,
  range.fraction = 0.2,
  enable.INLAspacetime = TRUE
)

inla.barrier.polygon(mesh, barrier.triangles, Omega = NULL)

inla.barrier.q(fem, ranges, sigma = 1, envir = NULL)

inla.barrier.fem(mesh, barrier.triangles, Omega = NULL)

Arguments

mesh

The mesh to build the model on, from inla.mesh.2d
barrier.triangles

The numerical ids of the triangles that make up the barrier area
prior.range

2 parameters ⁠(range0,Prange)⁠ for the prior spatial range. If Prange is NA, then range0 is used as a fixed range value (not tested).
prior.sigma

2 parameters ⁠(sig0,Psig)⁠ for the prior marginal standard deviation sigma. If Psig is NA, then sig0 is used as a fixed sigma value (not tested).
range.fraction

The length of the spatial range inside the barrier area, as a fraction of the range parameter.
enable.INLAspacetime

Use the implentation in the package INLAspacetime instead if available (default TRUE) if its available. You may need set this option to FALSE if you want to extract properties of the model for other use, like for example inla.rgeneric.q.
Omega

Advanced option for creating a set of permeable barriers (not documented)
fem

represents the Barrier model or the Different Terrains (DT) model, by containing all the needed matrices to solve the SPDE
ranges, sigma

the hyperparameters that determine Q
envir

the environment used for caching (with optimize=TRUE), if any

Details

This model is described in the ArXiv preprint arXiv:1608.03787. For examples, see https://haakonbakkagit.github.io/btopic128.html

  • inla.barrier.pcmatern This function creates the model component used in inla(...)

  • inla.barrier.polygon This function constructs SpatialPolygons for the different subdomains (areas)

  • inla.barrier.q: This function computes a specific precision matrix

  • inla.barrier.fem This function computes the Finite Element matrices that are needed to compute the precision matrix Q later

Value

  • inla.barrier.pcmatern gives the (rgeneric) model object for fitting the model in INLA

  • inla.barrier.polygon gives the polygon around the barrier (mainly for plotting)

  • inla.barrier.q is an internal method producing the Q matrix from a result of inla.barrier.fem,

  • inla.barrier.fem is an internal method producing the Finite Element matrices.

Author(s)

Haakon Bakka bakka@r-inla.org

See Also

inla.spde2.pcmatern

Install alternative binary builds.

Description

Install a new binary for os unless missing(os), for which the os is chosen interactively among the available builds.

Usage

inla.binary.install(
  os = c("CentOS Linux-6", "CentOS Linux-7", "CentOS Linux-8", "CentOS Stream-8",
    "Rocky Linux-8", "Rocky Linux-9", "Manjaro Linux-", "Fedora-33", "Fedora-34",
    "Fedora Linux-35", "Fedora Linux-36", "Fedora Linux-37", "Fedora Linux-38",
    "Fedora Linux-39", "Fedora Linux-40", "Fedora Linux-41", "Ubuntu-16.04",
    "Ubuntu-18.04", "Ubuntu-20.04", "Ubuntu-22.04", "Ubuntu-24.04"),
  path = NULL,
  verbose = TRUE,
  md5.check = TRUE,
  secure.http = TRUE
)

Arguments

os

If os is given, install binary build for this os. If os is not given, chose os interactively among available builds.
path

character. The install path. If NULL the path is derived from INLA package
verbose

Logical. Verbose output if TRUE
md5.check

Logical. If TRUE, stop if md5-checksum-file is not present or md5-checksum fail. If FALSE, ignore md5-checksum check.
secure.http

Logical. Use secure http (ie ⁠https://⁠) or ⁠http://⁠

Value

Return TRUE if installation was sucessful and FALSE if not.

Author(s)

Havard Rue hrue@r-inla.org

Examples

## Not run: 
     inla.binary.install()
     inla.binary.install(os = "CentOS Linux-7")
     inla.binary.install(os = "CentOS Linux-7",  path = "~/local/bin/inla.binary")
   
## End(Not run)

inla.changelog

Description

List the recent changes in the inla-program and its R-interface

Usage

inla.changelog()

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla()

Collect results from a inla-call

Description

inla.collect.results collect results from a inla-call

Usage

inla.collect.results(
  results.dir,
  debug = FALSE,
  only.hyperparam = FALSE,
  file.log = NULL,
  file.log2 = NULL,
  silent = inla.getOption("silent")
)

Arguments

results.dir

The directory where the results of the inla run are stored
debug

Logical. If TRUE some debugging information are printed
only.hyperparam

Binary variable indicating wheather only the results for the hyperparameters should be collected
file.log

Character. The filename, if any, of the logfile for the internal calculations
file.log2

Character. The filename, if any, of the logfile2 for the internal calculations
silent

Internal use only

Details

This function is mainly used inside inla to collect results after running the inla function. It can also be used to collect results into R after having run an inla section outside R.

Value

The function returns an object of class "inla", see the help file for inla for details.

Convert a Cox proportional hazard model into Poisson regression

Description

Tools to convert a Cox proportional hazard model into Poisson regression

Usage

inla.coxph(formula, data, control.hazard = list(), debug = FALSE, tag = "")

inla.rbind.data.frames(...)

Arguments

formula

The formula for the coxph model where the response must be a inla.surv-object.
data

All the data used in the formula, as a list.
control.hazard

Control the model for the baseline-hazard; see ?control.hazard.
debug

Print debug-information
tag

An optional tag added to the names of the new variables created (to make them unique when combined with several calls of inla.coxph. Note that E..coxph is not included, as its usually merged into one vector over different expansions.
...

Data.frames to be rbind-ed, padding with NA.

Value

inla.coxph returns a list of new expanded variables to be used in the inla-call. Note that element data and data.list needs to be merged into a list to be passed as the data argument. See the example for details.

inla.rbind.data.frames returns the rbinded data.frames padded with NAs. There is a better implementation in dplyr::bind_rows, which is used if package dplyr is installed.

Author(s)

Havard Rue hrue@r-inla.org

Examples

## How the cbind.data.frames works:
df1 = data.frame(x=1:2, y=2:3, z=3:4)
df2 = data.frame(x=3:4, yy=4:5, zz=5:6)
inla.rbind.data.frames(df1, df2)

## Standard example of how to convert a coxph into a Poisson regression
n = 1000
x = runif(n)
lambda = exp(1+x)
y = rexp(n, rate=lambda)
event = rep(1,n)
data = list(y=y, event=event, x=x)
y.surv = inla.surv(y, event)
intercept1 = rep(1, n)
p = inla.coxph(y.surv ~ -1 + intercept1 + x,
               list(y.surv = y.surv,  x=x, intercept1 = intercept1))

r = inla(p$formula,
        family = p$family,
        data=c(as.list(p$data), p$data.list),
        E = p$E)
summary(r)

## How to use this in a joint model
intercept2 = rep(1, n)
y = 1 + x + rnorm(n, sd=0.1)
df = data.frame(intercept2, x, y)

## new need to cbind the data.frames, and then add the list-part of
## the data
df.joint = c(as.list(inla.rbind.data.frames(p$data, df)), p$data.list)
df.joint$Y = cbind(df.joint$y..coxph, df.joint$y)

## merge the formulas, recall to add '-1' and to use the new joint
## reponse 'Y'
formula = update(p$formula, Y ~ intercept2 -1 + .)

rr = inla(formula,
        family = c(p$family, "gaussian"),
        data = df.joint,
        E = df.joint$E..coxph)


## A check that automatic and manual approach gives the same result
data(Leuk)
## add some random stuff for testing. Note that variables needs to
## be in 'data' as they are expanded
Leuk$off <- runif(nrow(Leuk), min = -0.5, max = 0.5)
Leuk$off.form <- runif(nrow(Leuk), min = -0.5, max = 0.5)
Leuk$w <- runif(nrow(Leuk), min = 0.5, max = 1.0)
formula <- inla.surv(time, cens) ~ sex + age +
    wbc + tpi + offset(off.form)
r <- inla(formula, family = "coxph", data = Leuk,
          offset = off, weights = w)
cph <- inla.coxph(formula = formula, data = Leuk)
cph.data = c(as.list(cph$data), cph$data.list)
rr <- inla(cph$formula, family = cph$family,  data = cph.data, 
           E = cph$E, offset = off, weights = w)
print(cbind(r$mlik, rr$mlik, r$mlik - rr$mlik))

Improved estimates for the CPO/PIT-values

Description

Improve the estimates of the CPO/PIT-values be recomputing the model-fit by removing data-points.

Usage

inla.cpo(
  result,
  force = FALSE,
  mc.cores = NULL,
  verbose = TRUE,
  recompute.mode = TRUE
)

Arguments

result

An object of class inla, ie a result of a call to inla()
force

If TRUE, then recompute all CPO/PIT values and not just those with result$cpo$failure > 0.
mc.cores

The number of cores to use in parallel::mclapply. If is.null(mc.cores), then check getOption("mc.cores") and inla.getOption("num.threads") in that order.
verbose

Run in verbose mode?
recompute.mode

Should be mode (and the integration points) be recomputed when a data-point is removed or not?

Value

The object returned is the same as result but the new improved estimates of the CPO/PIT values replaced.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla()

Examples

n = 10
y = rnorm(n)
r = inla(
  y ~ 1,
  data = data.frame(y),
  control.compute = list(cpo=TRUE),
  num.threads = "1:1" # Protect package testing from parallel execution
)

rr = inla.cpo(r, force=TRUE)

Create a coordinate reference system object

Description

[Deprecated] in favour of fmesher::fm_CRS()

Creates either a CRS object or an inla.CRS object, describing a coordinate reference system.

Usage

inla.CRS(..., args = NULL)

inla.wkt_predef()

Arguments

...

Arguments passed on to fmesher::fm_CRS(...).
args

list of named proj4 arguments.

Value

Either an sp::CRS object or an inla.CRS object, depending on if the coordinate reference system described by the parameters can be expressed with a pure sp::CRS object or not.

An S3 inla.CRS object is a list, usually (but not necessarily) containing at least one element:

crs

The basic sp::CRS object

inla.wkt_predef returns a WKT2 string defining a projection

inla.wkt_predef returns a WKT2 string defining a projection

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

fmesher::fm_crs(), fmesher::fm_wkt(), fmesher::fm_crs_is_identical()

Examples

if (require("sf")) {
    crs1 <- fmesher::fm_crs("longlat_globe")
    crs2 <- fmesher::fm_crs("lambert_globe")
    crs3 <- fmesher::fm_crs("mollweide_norm")
    crs4 <- fmesher::fm_crs("hammer_globe")
    crs5 <- fmesher::fm_crs("sphere")
    crs6 <- fmesher::fm_crs("globe")
}
## Not run: 
names(inla.wkt_predef())

## End(Not run)

## Not run: 
names(inla.wkt_predef())

## End(Not run)

Show expanded CRS arguments

Description

Wrapper for sp::CRS and inla.CRS objects to extract the coordinate reference system argument string. 'r lifecycle::badge("deprecated")' in favour of fmesher::fm_proj4string(), or fmesher::fm_wkt() for WKT2 representations.

Usage

inla.CRSargs(x, ...)

inla.as.CRSargs.list(x, ...)

inla.as.list.CRSargs(x, ...)

inla.as.list.CRS(x, ...)

inla.as.CRS.list(x, ...)

Arguments

x

An sp::CRS or inla.CRS object (for inla.CRSargs and inla.as.list.CRS), a character string (for inla.as.list.CRSargs), or a list (for inla.as.CRS.list and inla.as.CRSargs.list).
...

Additional arguments passed on to other methods.

Details

  • inla.as.CRSargs.list: CRS proj4 string for name=value pair list

  • inla.as.list.CRSargs: List of name=value pairs from CRS proj4 string

Value

For inla.CRSargs and inla.as.CRSargs.list, a character string with PROJ.4 arguments.

For inla.as.list.CRS and inla.as.list.CRSargs, a list of name/value pairs.

For inla.as.CRS.list, a CRS or inla.CRS object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.CRS()

Examples

if (require("sf") && require("sp") && require("fmesher")) {
    crs0 <- fm_CRS("longlat_norm")
    p4s <- fm_proj4string(crs0)
    lst <- inla.as.list.CRSargs(p4s)
    crs1 <- inla.as.CRS.list(lst)
    lst$a <- 2
    crs2 <- fm_CRS(p4s, args = lst)
    print(fm_proj4string(crs0))
    print(fm_proj4string(crs1))
    print(fm_proj4string(crs2))
}

Opens a new device

Description

Open a new device using dev.new() unless using RStudio

Usage

inla.dev.new(...)

Arguments

...

Optional arguments to dev.new()

Value

The value of dev.new() if not running RStudio, otherwise NULL

Author(s)

Havard Rue hrue@r-inla.org

Diameter of a point set

Description

[Deprecated] Use fmesher::fm_diameter() instead.

Find an upper bound to the convex hull of a point set

Usage

inla.diameter(x, ...)

Arguments

x

A point set as an n×dn\times d matrix, or an fmesher::fm_mesh_2d()] related object.
...

Additional parameters passed on to fmesher::fm_diameter().

Value

A scalar, upper bound for the diameter of the convex hull of the point set.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Examples

inla.diameter(matrix(c(0, 1, 1, 0, 0, 0, 1, 1), 4, 2))

View documentation

Description

View documentation of latent, prior and likelihood models.

Usage

inla.doc(what, section, verbose = FALSE)

Arguments

what

What to view documentation about; name of latent model, name of prior, etc. (A regular expression.)
section

An optional section, like names(inla.models()), to look for the documentation. If missing, all sections are used.
verbose

Logical If TRUE then run in verbose mode

Author(s)

Havard Rue hrue@r-inla.org

See Also

www.r-inla.org

Examples

## Not run: inla.doc("rw2")
## Not run: inla.doc("gaussian", section = "prior")

Return the path to the cgeneric-library for a pre-compiled external package

Description

Return the path to the cgeneric-library for a pre-compiled external package

Usage

inla.external.lib(package)

Arguments

package

the name of a package, given as a name or literal character string

Value

This function returns the complete path or NULL if file does not exists

Author(s)

Havard Rue hrue@r-inla.org

Extract elements by matching name from container objects.

Description

Extract elements by wildcard name matching from a data.frame, list, or matrix.

Usage

inla.extract.el(M, ...)

## S3 method for class 'matrix'
inla.extract.el(M, match, by.row = TRUE, ...)

## S3 method for class 'data.frame'
inla.extract.el(M, match, by.row = TRUE, ...)

## S3 method for class 'list'
inla.extract.el(M, match, ...)

Arguments

M

A container object.
...

Additional arguments, not used.
match

A regex defining the matching criterion.
by.row

If TRUE, extract data by row, otherwise by column.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Compute various mesh related quantities.

Description

[Deprecated] Use the methods in the fmesher package instead; see details below.

Low level function for computing finite element matrices, spherical harmonics, B-splines, and point mappings with barycentric triangle coordinates.

Usage

inla.fmesher.smorg(
  loc,
  tv,
  fem = NULL,
  aniso = NULL,
  gradients = FALSE,
  sph0 = deprecated(),
  sph = deprecated(),
  bspline = NULL,
  points2mesh = NULL,
  splitlines = NULL,
  output = NULL,
  keep = FALSE
)

Arguments

loc

3-column triangle vertex coordinate matrix.
tv

3-column triangle vertex index matrix.
fem

[Deprecated] Use fmesher::fm_fem() instead. Maximum finite element matrix order to be computed.
aniso

[Deprecated] Use fmesher::fm_fem() instead. A two-element list with γ\gamma and vv for an anisotropic operator ∇⋅H∇\nabla\cdot H \nabla, where H=γI+vv⊤H=\gamma I + v v^\top
gradients

[Deprecated] Use fmesher::fm_fem() instead. When TRUE, calculate derivative operator matrices dx, dy, and dz.
sph0

[Deprecated] Use fmesher::fm_raw_basis() instead.
sph

[Deprecated] Use fmesher::fm_raw_basis() instead.
bspline

[Deprecated] Use fmesher::fm_raw_basis() instead. Rotationally invariant B-splines on a sphere. 3-vector with number of basis functions n, basis degree degree, and a logical; TRUE uniform knot angles, FALSE for uniform spacing in sin⁡(latitude)\sin(latitude).
points2mesh

[Deprecated] Use fmesher::fm_bary() instead. 3-column matrix with points to be located in the mesh.
splitlines

[Deprecated] Use fmesher::fm_split_lines() or fmesher::fmesher_split_lines() instead. A list with elements loc (3-column coordinate matrix) and idx (2-column index matrix) describing line segments that are to be split into sub-segments at triangle boundaries.
output

Names of objects to be included in the output, if different from defaults.
keep

When TRUE, for debugging purposes keep the fmesher I/O files on disk.

Value

A list of generated named quantities.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Generate text RGB color specifications.

Description

[Deprecated] Use fmesher::fm_generate_colors() instead.

Generates a tex RGB color specification matrix based on a color palette.

Usage

inla.generate.colors(
  color,
  color.axis = NULL,
  color.n = 512,
  color.palette = cm.colors,
  color.truncate = FALSE,
  alpha = NULL
)

Arguments

color

character, matrix or vector
color.axis

The min/max limit values for the color mapping.
color.n

The number of colors to use in the color palette.
color.palette

A color palette function.
color.truncate

If TRUE, truncate the colors at the color axis limits.
alpha

Transparency/opaqueness values.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Return the internal environment used by INLA

Description

A function which return the internal environment used by INLA

Usage

inla.get.inlaEnv()

Value

This function returns the internal environment used by INLA to keep internal variables.

Author(s)

Havard Rue hrue@r-inla.org

Group or cluster covariates

Description

inla.group group or cluster covariates so to reduce the number of unique values

Usage

inla.group(x, n = 25, method = c("cut", "quantile"), idx.only = FALSE)

Arguments

x

The vector of covariates to group.
n

Number of classes or bins to group into.
method

Group either using bins with equal length intervals (method = "cut"), or equal distance in the ⁠probability' scale using the quantiles (⁠method = "quantile"').
idx.only

Option to return the index only and not the method.

Value

inla.group return the new grouped covariates where the classes are set to the median of all the covariates belonging to that group.

Author(s)

Havard Rue hrue@r-inla.org

See Also

f()

Examples

## this gives groups 3 and 8
x = 1:10
x.group = inla.group(x, n = 2)

## this is the intended use, to reduce the number of unique values in
## the of first argument of f()
n = 100
x = rnorm(n)
y = x + rnorm(n)
result = inla(y ~ f(inla.group(x, n = 20), model = "iid"), data=data.frame(y=y,x=x))

Compute group.cv-values

Description

From a fitted model, compute and add the group.cv-values

Usage

inla.group.cv(
  result,
  group.cv = NULL,
  num.level.sets = -1,
  strategy = c("posterior", "prior"),
  size.max = 32,
  groups = NULL,
  selection = NULL,
  group.selection = NULL,
  friends = NULL,
  weights = NULL,
  verbose = FALSE,
  epsilon = 0.005,
  prior.diagonal = 1e-04,
  keep = NULL,
  remove = NULL,
  remove.fixed = TRUE
)

Arguments

result

An object of class inla, ie a result of a call to inla().
group.cv

If given, the groups are taken from this argument. group.cv must be the output of previous call to inla.group.cv().
num.level.sets

Number of level.sets to use. The default value -1 corresponds to leave-one-out cross-validation. If argument weights is used, then this is threshold for the sum of the weights defining a group.
strategy

One of "posterior" or "prior". See the vignette for details.
size.max

The maximum size (measure in the number of nodes) of a group. If the computed group-size is larger, it will be truncated to size.max. Note that: If weights are in use, then this still corresponds to the number of nodes in the group, and not the sum of the weights. This is ment as an emergency option to avoid the size of the group to go nuts.
groups

An (optional) predefined list of groups. See the vignette for details.
selection

An optional list of data-indices to use. If not given, then all data are used.
group.selection

An optional list of data-indices to use when building the groups. If given, each group beyond the observation itself, must be a subset of group.selection. If not given, then all data are used.
friends

An optional list of lists of indices to use a friends
weights

An optional positive weight attached to each datapoint. The sume of the weights define the size of the group. If NULL, then unit weight is used.
verbose

Run with verbose output of some of the internals in the calculations. This option will also enable inla(..., verbose=TRUE) if its not enabled already.
epsilon

Two correlations with a difference less than epsilon, will be classified as identical.
prior.diagonal

When strategy="prior", prior.diagonal is added to the diagonal of the prior precision matrix to avoid singularities
keep

For strategy="prior", then this gives a vector of the name of model-components TO USE when computing the groups. See the vignette for details. Not both of keep and remove can be defined.
remove

For strategy="prior", then this gives a vector of the name of model-components NOT TO USE when computing the groups. See the vignette for details. Not both of keep and remove can be defined.
remove.fixed

For strategy="prior", this is the default option which is in effect if both keep and remove are NULL. If TRUE, it will remove (or condition on) all fixed effects when computing the groups. See the vignette for details.

Value

The object returned is list related to leave-group-out cross-validation. See the vignette for details.

Author(s)

Havard Rue hrue@r-inla.org

See Also

control.compute()

PROJ6 detection

Description

Detect whether PROJ6 is available for INLA. Deprecated and always returns TRUE.

Usage

inla.has_PROJ6()

inla.not_for_PROJ6(fun)

inla.not_for_PROJ4(fun)

inla.fallback_PROJ6(fun)

inla.requires_PROJ6(fun)

Arguments

fun

The name of the calling function

Details

inla.has_PROJ6 is called to check if PROJ6&GDAL3 are available.

Value

For inla.has_PROJ6, always returns TRUE. Previously: logical; TRUE if PROJ6 is available, FALSE otherwise

Functions

  • inla.has_PROJ6(): [Deprecated]

  • inla.not_for_PROJ6(): [Deprecated] Called to warn about using old PROJ4 features even though PROJ6 is available

  • inla.not_for_PROJ4(): [Deprecated] Called to give an error when calling methods that are only available for PROJ6

  • inla.fallback_PROJ6(): [Deprecated] Called to warn about falling back to using old PROJ4 methods when a PROJ6 method hasn't been implemented

  • inla.requires_PROJ6(): [Deprecated] Called to give an error when PROJ6 is required but not available

Examples

## Not run: 
inla.has_PROJ6()

## End(Not run)

Improved estimates for the hyperparameters (classic-mode only)

Description

Improve the estimates of the posterior marginals for the hyperparameters of the model using the grid integration strategy. (classic-mode only)

Usage

inla.hyperpar(
  result,
  skip.configurations = TRUE,
  verbose = FALSE,
  dz = 0.75,
  diff.logdens = 15,
  h = NULL,
  restart = FALSE,
  quantiles = NULL,
  keep = FALSE
)

Arguments

result

An object of class inla, ie a result of a call to inla() in classic mode
skip.configurations

A boolean variable; skip configurations if the values at the main axis are to small. (Default TRUE)
verbose

Boolean indicating wheather the inla program should run in a verbose mode.
dz

Step length in the standardized scale used in the construction of the grid, default 0.75.
diff.logdens

The difference of the log.density for the hyperpameters to stop numerical integration using int.strategy='grid'. Default 15
h

The step-length for the gradient calculations for the hyperparameters. Default 0.01.
restart

A boolean defining whether the optimizer should start again to ind the mode or if it should use the mode contained in the object
quantiles

A vector of quantiles, to compute for each posterior marginal.
keep

A boolean variable indicating the working files (ini file, data files and results files) should be kept

Value

The object returned is the same as object but the estimates of the hyperparameters are replaced by improved estimates.

Note

This function might take a long time or if the number of hyperparameters in the model is large. If it complains and says ⁠I cannot get enough memory⁠, try to increase the value of the argument dz or decrease diff.logdens.

Author(s)

Havard Rue hrue@r-inla.org

References

See the references in inla

See Also

inla()

Produce samples from the approximated joint posterior for the hyperparameters

Description

Produce samples from the approximated joint posterior for the hyperparameters

Usage

inla.hyperpar.sample(n, result, intern = FALSE, improve.marginals = FALSE)

Arguments

n

Integer. Number of samples required.
result

An inla-object, f.ex the output from an inla-call.
intern

Logical. If TRUE then produce samples in the internal scale for the hyperparmater, if FALSE then produce samples in the user-scale. (For example log-precision (intern) and precision (user-scale))
improve.marginals

Logical. If TRUE, then improve the samples taking into account possible better marginal estimates for the hyperparameters in result.

Value

A matrix where each sample is a row. The contents of the column is described in the rownames.

Author(s)

Havard Rue hrue@r-inla.org

Examples

n = 100
r = inla(y ~ 1 + f(idx), data = data.frame(y=rnorm(n), idx = 1:n))
ns = 500
x = inla.hyperpar.sample(ns, r)

rr = inla.hyperpar(r)
xx = inla.hyperpar.sample(ns, rr, improve.marginals=TRUE)

Test CRS and inla.CRS for equality

Description

[Deprecated] Use fmesher::fm_crs_is_identical() instead.

Wrapper for identical, optionally testing only the CRS part of two objects Deprecated in favour of fmesher::fm_crs_is_identical()

Usage

inla.identical.CRS(...)

Arguments

...

Arguments passed on to fmesher::fm_crs_is_identical()

Provide samples from the iidkd component (experimental)

Description

This function provide samples of the iidkd component using more interpretable parameters

Usage

inla.iidkd.sample(n = 10^4, result, name, return.cov = FALSE)

Arguments

n

Integer Number of samples to use
result

inla-object An object of class inla, ie a result of a call to inla()
name

Character The name of the iidkd component
return.cov

Logical Return samples of the covariance matrix instead of stdev/correlation matrix described below?

Value

A list of sampled matrices, with (default) correlations on the off-diagonal and standard-deviations on the diagonal

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("iidkd")

Spacetime interaction models

Description

It implements the models in Knorr-Held, L. (2000) with three different constraint approaches: sum-to-zero, contrast or diagonal add.

Usage

inla.knmodels(
  formula,
  progress = FALSE,
  control.st = list(time, space, spacetime, graph, type = c(paste(1:4), paste0(2:4, "c"),
    paste0(2:4, "d")), diagonal = 1e-05, timeref = 1, spaceref = 1),
  ...,
  envir = parent.frame()
)

Arguments

formula

The formula specifying the other model components, without the spacetime interaction term. The spacetime interaction term will be added accordly to the specification in the control.st argument. See inla
progress

If it is to be shown the model fitting progress. Useful if more than one interaction type is being fitted.
control.st

Named list of arguments to control the spacetime interaction. It should contain:

time

to be used as the index set for the main temporal effect which will be considered for the constraints when it is the case.

space

to be used as the index set for the main spatial effect which will be considered for the constraints when it is the case.

spacetime

to be the index set for the spacetime interaction effect.

graph

to be the graph for the spatial neighbor structure to be used in a f() term for the main spatial random effect term or for building the spacetime interaction model.

type

to specify the spacetime interaction type. 1 to 4 corresponds to the four interaction types in Knorr-Held, L. (2000) with all the needed sum-to-zero constraints. ⁠2c⁠, ⁠3c⁠ and ⁠4c⁠ are the contrast version considering the first time or space constrained to be equal to zero. ⁠2d⁠, ⁠3d⁠ and ⁠4d⁠ are the corresponding versions when considering the diagonal add approach.

diagonal

to be the value to be added to the diagonal when using the diagonal add approach.

timeref

to specify the time point to be the reference time in the contrast parametrization.

spaceref

to specify the area to be the reference for the contrast parametrization.

...

where additional arguments can be passed to f() function. Specification of the hyperparameter, fixed or random, initial value, prior and its parameters for the spacetime interaction. See ?inla.models and look for generic0. By default we scale it and use the PC-prior to set the prior using the pc.prec prior with param = c(0.5, 0.5). See documentation with ?inla.doc("pc.prec").
...

Arguments to be passed to the inla() function.
envir

Environment in which to evaluate the ... arguments.

Value

inla.knmodels returns an object of class "inla". or a list of objects of this class if it is asked to compute more than one interaction type at once. Note: when the model type is 2c, 3c, 4c, 2d, 3d or 4d, it also includes linear combinations summary.

Author(s)

Elias T. Krainski

See Also

inla.knmodels.sample() to sample from

Examples

### define space domain as a grid
grid <- sp::SpatialGrid(sp::GridTopology(c(0,0), c(1, 1), c(4, 5)))
(n <- nrow(xy <- sp::coordinates(grid)))

### build a spatial neighborhood list
jj <- lapply(1:n, function(i)
    which(sqrt((xy[i,1]-xy[,1])^2 + (xy[i,2]-xy[,2])^2)==1))

### build the spatial adjacency matrix
graph <- sparseMatrix(rep(1:n, sapply(jj, length)),
                      unlist(jj), x=1, dims=c(n, n))

### some random data at 10 time point
dat <- inla.knmodels.sample(graph, m=10, tau.t=2, tau.s=2, tau.st=3)
str(dat)
sapply(dat$x, summary)

nd <- length(dat$x$eta)
dat$e <- runif(nd, 0.9, 1.1)*rgamma(n, 40, 2)
dat$y <- rpois(nd, dat$e*exp(dat$x$eta-3))
summary(dat$y)

### fit the type 4 considering three different approaches
tgraph <- sparseMatrix(i=c(2:10, 1:9), j=c(1:9, 2:10), x=1)
res <- inla.knmodels(y ~ f(time, model='bym2', graph=tgraph) +
     f(space, model='bym2', graph=graph),
     data=dat, family='poisson', E=dat$E, progress=TRUE,
     control.st=list(time=time, space=space,
        spacetime=spacetime, graph=graph, type=c(4, '4c')), 
     control.compute=list(dic=TRUE, waic=TRUE, cpo=TRUE))
sapply(res, function(x)
       c(dic=x$dic$dic, waic=x$waic$waic, cpo=-sum(log(x$cpo$cpo))))

Spacetime interaction models sampler function

Description

It implements the sampling method for the models in Knorr-Held, L. (2000) considering the algorithm 3.1 in Rue & Held (2005) book.

Usage

inla.knmodels.sample(
  graph,
  m,
  type = 4,
  intercept = 0,
  tau.t = 1,
  phi.t = 0.7,
  tau.s = 1,
  phi.s = 0.7,
  tau.st = 1,
  ev.t = NULL,
  ev.s = NULL
)

Arguments

graph

Model graph definition
m

Time dimention.
type

Integer from 1 to 4 to identify one of the four interaction type.
intercept

A constant to be added to the linear predictor
tau.t

Precision parameter for the main temporal effect.
phi.t

Mixing parameter in the bym2 model assumed for the main temporal effect.
tau.s

Precision parameter for the main spatial effect.
phi.s

Mixing parameter in the bym2 model assumed for the main spatial effect.
tau.st

Precision parameter for the spacetime effect.
ev.t

Eigenvalues and eigenvectors of the temporal precision matrix structure.
ev.s

Eigenvalues and eigenvectors of the spatial precision matrix structure.

Value

A list with the following elements

time

The time index for each obervation, with length equals m*n.
space

The spatial index for each observation, with length equals m*n.
spacetime

The spacetime index for each obervation, with length equals m*n.
x

A list with the following elements
t.iid

The unstructured main temporal effect part.
t.str

The structured main temporal effect part.
t

The main temporal effect with length equals ⁠2m⁠.
s.iid

The unstructured main spatial effect part.
s.str

The structured main spatial effect part.
s

The main spatial effect with length equals ⁠2n⁠.
st

The spacetime interaction effect with length m*n.
eta

The linear predictor with length n*m.

Author(s)

Elias T. Krainski

See Also

inla.knmodels() for model fitting

Kolmogorov-Smirnov Test Plots

Description

Illustrate a one-sample Kolmogorov-Smirnov test by plotting the empirical distribution deviation.

Usage

inla.ks.plot(x, y, diff = TRUE, ...)

Arguments

x

a numeric vector of data values.
y

a cumulative distribution function such as 'pnorm'.
diff

logical, indicating if the normalised difference should be plotted. If FALSE, the absolute distribution functions are plotted.
...

additional arguments for ks.test(), ignored in the plotting. In particular, only two-sided tests are illustrated.

Details

In addition to the (normalised) empirical distribution deviation, lines for the K-S test statistic are drawn, as well as ±\pm two standard deviations around the expectation under the null hypothesis.

Value

A list with class "htest", as generated by ks.test()

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

ks.test()

Examples

## Check for N(0,1) data
data = rowSums(matrix(runif(100*12)*2-1,100,12))/2
inla.ks.plot(data, pnorm)

## Not run: 
## Check the goodness-of-fit of cross-validated predictions
result = inla(..., control.predictor=list(cpo=TRUE))
inla.ks.plot(result$pit, punif)

## End(Not run)

Providing functions for sampling new data, evaluating pdf, cdf, and quantiles for new data.

Description

This function return function to compute the pdf,cdf,quantiles, or samples for new data using the likelihood from a inla-object.

Usage

inla.likelihood(type = c("d", "p", "r", "q", "s"), args)

Arguments

type

The returned function type. The definition is similar to "rnorm","dnorm","pnorm",and "dnorm".
args

It is usually a return value from "inla.likelihood.parser", which specifies parameters, link function and transformation function of hyperparameters.

Value

value goes here

Author(s)

Havard Rue hrue@r-inla.org

List available model components, likelihoods, priors, etc

Description

List available model components, likelihoods, priors, etc. To read specific documentation for the individual elements, use inla.doc().

The list is cat'ed with ... arguments.

This function is EXPERIMENTAL.

Usage

inla.list.models(section = names(inla.models()), ...)

Arguments

section

The section(s) to list, missing section will list all sections. names(inla.models()) lists available sections.
...

Additional argument to cat

Value

Nothing is returned

Author(s)

Havard Rue

Examples

## Not run: 
inla.list.models("likelihood")
inla.list.models(c("prior", "group"))
inla.list.models(file=file("everything.txt"))

#Show detailed doc for a specific prior/likelihood/latent model
inla.doc("binomial")

## End(Not run)

Numerical evaluation of Matern and related covariance functions.

Description

Calculates covariance and correlation functions for Matern models and related oscillating SPDE models, on RdR^d and on the sphere, S2S^2.

Usage

inla.matern.cov(
  nu,
  kappa,
  x,
  d = 1,
  corr = FALSE,
  norm.corr = FALSE,
  theta,
  epsilon = 1e-08
)

inla.matern.cov.s2(nu, kappa, x, norm.corr = FALSE, theta = 0, freq.max = NULL)

Arguments

nu

The Matern smoothness parameter.
kappa

The spatial scale parameter.
x

Distance values.
d

Space dimension; the domain is RdR^d.
corr

If TRUE, calculate correlations, otherwise calculate covariances. Only used for pure Matern models (i.e. with θ=0\theta=0).
norm.corr

If TRUE, normalise by the estimated variance, giving approximate correlations.
theta

Oscillation strength parameter.
epsilon

Tolerance for detecting points close to distance zero.
freq.max

The maximum allowed harmonic order. Current default 40, to be changed to a dynamic choice based on error bounds.

Details

On RdR^d, the models are defined by the spectral density given by

S(w)=1(2π)d(κ4+2κ2cos⁡(πθ)∣w∣2+∣w∣4)(ν+d/2)/2S(w) = \frac{1}{(2\pi)^d (\kappa^4 + 2 \kappa^2 \cos(\pi \theta) |w|^2 + |w|^4)^{(\nu + d/2)/2}}

On S2S^2, the models are defined by the spectral coefficients

S(k)=2k+14π(κ4+2κ2cos⁡(πθ)k(k+1)+k2(k+1)2)(ν+1)/2S(k) = \frac{2k+1}{4\pi (\kappa^4 + 2 \kappa^2 \cos(\pi \theta) k(k+1) + k^2(k+1)^2)^{(\nu + 1)/2}}

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Create an mdata-object for INLA

Description

This defines an mdata-object for matrix valued response-families

Usage

inla.mdata(y, ...)

## S3 method for class 'inla.mdata'
print(x, ...)

as.inla.mdata(object)

is.inla.mdata(object)

Arguments

y

The response vector/matrix
...

Additional vectors/matrics of same length as y
x

An mdata object
object

Any R-object

Value

An object of class inla.mdata. There is method for print.

is.inla.mdata returns TRUE if object inherits from class inla.mdata, otherwise FALSE.

as.inla.mdata returns an object of class inla.mdata

Note

It is often required to set Y=inla.mdata(...) and then define the formula as Y~..., especially when used with inla.stack.

Author(s)

Havard Rue

See Also

inla()

Function space definition objects for 1D SPDE models.

Description

[Deprecated] Use fmesher::fm_mesh_1d() instead.

Create a 1D mesh specification inla.mesh.1d object, that defines a function space for 1D SPDE models.

Usage

inla.mesh.1d(
  loc,
  interval = range(loc),
  boundary = NULL,
  degree = 1,
  free.clamped = FALSE,
  ...
)

inla.mesh.1d.fem(mesh)

Arguments

loc

B-spline knot locations.
interval

Interval domain endpoints.
boundary

Boundary condition specification. Valid conditions are c('neumann', 'dirichlet', 'free', 'cyclic'). Two separate values can be specified, one applied to each endpoint.
degree

The B-spline basis degree. Supported values are 0, 1, and 2.
free.clamped

If TRUE, for 'free' boundaries, clamp the basis functions to the interval endpoints.
...

Additional option, currently unused.
mesh

An inla.mesh.1d object

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Mapping matrix for 1D meshes

Description

[Deprecated] Use fmesher::fm_basis() instead.

Calculates barycentric coordinates and weight matrices for inla.mesh.1d() objects.

Usage

inla.mesh.1d.bary(mesh, loc, method = c("linear", "nearest"))

inla.mesh.1d.A(mesh, loc, weights = NULL, derivatives = NULL, method = NULL)

Arguments

mesh

An inla.mesh.1d() object.
loc

Coordinate values.
method

Interpolation method. If not specified for inla.mesh.1d.A (recommended), it is determined by the mesh basis function properties.
weights

Weights to be applied to the A matrix rows.
derivatives

If TRUE, also compute derivative weight matrices dA and d2A.

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

High-quality triangulations

Description

[Deprecated] Use fmesher::fm_mesh_2d_inla() instead.

Create a triangle mesh based on initial point locations, specified or automatic boundaries, and mesh quality parameters.

Usage

inla.mesh.2d(
  loc = NULL,
  loc.domain = NULL,
  offset = NULL,
  n = NULL,
  boundary = NULL,
  interior = NULL,
  max.edge = NULL,
  min.angle = NULL,
  cutoff = 1e-12,
  max.n.strict = NULL,
  max.n = NULL,
  plot.delay = NULL,
  crs = NULL
)

Arguments

loc

Matrix of point locations to be used as initial triangulation nodes. Can alternatively be a SpatialPoints or SpatialPointsDataFrame object.
loc.domain

Matrix of point locations used to determine the domain extent. Can alternatively be a SpatialPoints or SpatialPointsDataFrame object.
offset

The automatic extension distance. One or two values, for an inner and an optional outer extension. If negative, interpreted as a factor relative to the approximate data diameter (default=-0.10???)
n

The number of initial nodes in the automatic extensions (default=16)
boundary

A list of one or two inla.mesh.segment() objects describing domain boundaries.
interior

An inla.mesh.segment() object describing desired interior edges.
max.edge

The largest allowed triangle edge length. One or two values.
min.angle

The smallest allowed triangle angle. One or two values. (Default=21)
cutoff

The minimum allowed distance between points. Point at most as far apart as this are replaced by a single vertex prior to the mesh refinement step.
max.n.strict

The maximum number of vertices allowed, overriding min.angle and max.edge (default=-1, meaning no limit). One or two values, where the second value gives the number of additional vertices allowed for the extension.
max.n

The maximum number of vertices allowed, overriding max.edge only (default=-1, meaning no limit). One or two values, where the second value gives the number of additional vertices allowed for the extension.
plot.delay

On Linux (and Mac if appropriate X11 libraries are installed), specifying a nonnegative numeric value activates a rudimentary plotting system in the underlying fmesher program, showing the triangulation algorithm at work, with waiting time factor plot.delay between each step.

On all systems, specifying any negative value activates displaying the result after each step of the multi-step domain extension algorithm.
crs

An optional CRS or inla.CRS object

Value

An inla.mesh object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.create(), inla.delaunay(), inla.nonconvex.hull()

Examples

loc <- matrix(runif(10 * 2), 10, 2)

if (require("splancs")) {
  boundary <- list(
    inla.nonconvex.hull(loc, 0.1, 0.15),
    inla.nonconvex.hull(loc, 0.2, 0.2)
  )
  offset <- NULL
} else {
  boundary <- NULL
  offset <- c(0.1, 0.2)
}
mesh <- inla.mesh.2d(loc, boundary = boundary, offset = offset, max.edge = c(0.05, 0.1))

plot(mesh)

Interactive mesh building and diagnostics

Description

Assess the finite element approximation errors in a mesh for interactive R sessions. More detailed assessment tools are in meshbuilder().

Usage

inla.mesh.assessment(mesh, spatial.range, alpha = 2, dims = c(500, 500))

Arguments

mesh

An inla.mesh
spatial.range

numeric; the spatial range parameter to use for the assessment
alpha

numeric; A valid inla.spde2.pcmatern alpha parameter
dims

2-numeric; the grid size

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

fmesher::fm_mesh_2d(), fmesher::fm_rcdt_2d(), meshbuilder

Examples

library(fmesher)
bnd <- fm_segm(cbind(
    c(0, 10, 10, 0, 0),
    c(0, 0, 10, 10, 0)
), is.bnd = TRUE)
mesh <- fm_mesh_2d_inla(boundary = bnd, max.edge = 1)
out <- inla.mesh.assessment(mesh, spatial.range = 3, alpha = 2)

Basis functions for inla.mesh

Description

[Deprecated] Use fmesher::fm_raw_basis() instead.

Calculate basis functions on a 1d or 2d inla.mesh()

Usage

inla.mesh.basis(
  mesh,
  type = "b.spline",
  n = 3,
  degree = 2,
  knot.placement = "uniform.area",
  rot.inv = TRUE,
  boundary = "free",
  free.clamped = TRUE,
  ...
)

Arguments

mesh

An inla.mesh.1d or inla.mesh object.
type

b.spline (default) for B-spline basis functions, sph.harm for spherical harmonics (available only for meshes on the sphere)
n

For B-splines, the number of basis functions in each direction (for 1d meshes n must be a scalar, and for planar 2d meshes a 2-vector). For spherical harmonics, n is the maximal harmonic order.
degree

Degree of B-spline polynomials. See inla.mesh.1d().
knot.placement

For B-splines on the sphere, controls the latitudinal placements of knots. "uniform.area" (default) gives uniform spacing in sin(latitude), "uniform.latitude" gives uniform spacing in latitudes.
rot.inv

For spherical harmonics on a sphere, rot.inv=TRUE gives the rotationally invariant subset of basis functions.
boundary

Boundary specification, default is free boundaries. See inla.mesh.1d() for more information.
free.clamped

If TRUE and boundary is "free", the boundary basis functions are clamped to 0/1 at the interval boundary by repeating the boundary knots.
...

Unused

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.1d() inla.mesh.2d()

Examples

n <- 100
loc <- matrix(runif(n * 2), n, 2)
mesh <- inla.mesh.2d(loc, max.edge = 0.05)
basis <- inla.mesh.basis(mesh, n = c(4, 5))

proj <- inla.mesh.projector(mesh)
image(proj$x, proj$y, inla.mesh.project(proj, basis[, 7]))

if (require(rgl)) {
  plot(mesh, rgl = TRUE, col = basis[, 7], draw.edges = FALSE, draw.vertices = FALSE)
}

Constraint segment extraction for inla.mesh

Description

[Deprecated] Use fmesher::fm_segm() instead.

Constructs an list of inla.mesh.segment object from boundary or interior constraint information in an inla.mesh() object.

Usage

inla.mesh.boundary(mesh, grp = NULL)

inla.mesh.interior(mesh, grp = NULL)

Arguments

mesh

An inla.mesh object.
grp

Group indices to extract. If NULL, all boundary/interior constrain groups are extracted.

Value

A list of inla.mesh.segment objects.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.segment(), inla.mesh.create(), inla.mesh.create.helper()

Examples

loc <- matrix(runif(100 * 2) * 1000, 100, 2)
mesh <- fmesher::fm_mesh_2d_inla(loc.domain = loc, max.edge = c(50, 500))
boundary <- inla.mesh.boundary(mesh)
interior <- inla.mesh.interior(mesh)

Compute connected mesh subsets

Description

[Deprecated] Compute subsets of vertices and triangles in an inla.mesh object that are connected by edges. This function is deprecated from INLA ⁠25.4.10⁠ when fmesher version ⁠0.3.0.9005⁠ or later is installed, which has fm_mesh_components().

Usage

inla.mesh.components(mesh)

Arguments

mesh

An fm_mesh_2d object

Value

A list with elements vertex and triangle, vectors of integer labels for which connected component they belong, and info, a data.frame with columns

component

Connected component integer label.
nV

The number of vertices in the component.
nT

The number of triangles in the component.
area

The surface area associated with the component. Component labels are not comparable across different meshes, but some ordering stability is guaranteed by initiating each component from the lowest numbered triangle whenever a new component is initiated.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

fmesher::fm_mesh_2d(), fmesher::fm_rcdt_2d()

Examples

# Construct two simple meshes:
library(fmesher)
loc <- matrix(c(0, 1, 0, 1), 2, 2)
mesh1 <- fm_mesh_2d(loc = loc, max.edge = 0.1)
bnd <- fm_nonconvex_hull_inla(loc, 0.3)
mesh2 <- fm_mesh_2d(boundary = bnd, max.edge = 0.1)

# Compute connectivity information:
conn1 <- inla.mesh.components(mesh1)
conn2 <- inla.mesh.components(mesh2)
# One component, simply connected mesh
conn1$info
# Two disconnected components
conn2$info

Low level function for high-quality triangulations

Description

[Deprecated] in favour of fmesher::fm_rcdt_2d_inla().

Create a constrained refined Delaunay triangulation (CRDT) for a set of spatial locations.

inla.mesh.create generates triangular meshes on subsets of R2R^2 and S2S^2. Use the higher level wrapper function inla.mesh.2d() for greater control over mesh resolution and coarser domain extensions.

inla.delaunay is a wrapper function for obtaining the convex hull of a point set and calling inla.mesh.create to generate the classical Delaunay tringulation.

Usage

inla.mesh.create(
  loc = NULL,
  tv = NULL,
  boundary = NULL,
  interior = NULL,
  extend = (missing(tv) || is.null(tv)),
  refine = FALSE,
  lattice = NULL,
  globe = NULL,
  cutoff = 1e-12,
  plot.delay = NULL,
  data.dir = NULL,
  keep = (!missing(data.dir) && !is.null(data.dir)),
  timings = FALSE,
  quality.spec = NULL,
  crs = NULL
)

inla.delaunay(loc, ...)

Arguments

loc

Matrix of point locations. Can alternatively be a SpatialPoints or SpatialPointsDataFrame object.
tv

A triangle-vertex index matrix, specifying an existing triangulation.
boundary

A list of inla.mesh.segment objects, generated by inla.mesh.segment(), specifying boundary constraint segments.
interior

A list of inla.mesh.segment objects, generated by inla.mesh.segment(), specifying interior constraint segments.
extend

logical or list specifying whether to extend the data region, with parameters

list("n")

the number of edges in the extended boundary (default=8)

list("offset")

the extension distance. If negative, interpreted as a factor relative to the approximate data diameter (default=-0.10)

Setting to FALSE is only useful in combination lattice or boundary.
refine

logical or list specifying whether to refine the triangulation, with parameters

list("min.angle")

the minimum allowed interior angle in any triangle. The algorithm is guaranteed to converge for min.angle at most 21 (default=21)

list("max.edge")

the maximum allowed edge length in any triangle. If negative, interpreted as a relative factor in an ad hoc formula depending on the data density (default=Inf)

list("max.n.strict")

the maximum number of vertices allowed, overriding min.angle and max.edge (default=-1, meaning no limit)

list("max.n")

the maximum number of vertices allowed, overriding max.edge only (default=-1, meaning no limit)
lattice

An inla.mesh.lattice object, generated by inla.mesh.lattice(), specifying points on a regular lattice.
globe

Subdivision resolution for a semi-regular spherical triangulation with equidistant points along equidistant latitude bands.
cutoff

The minimum allowed distance between points. Point at most as far apart as this are replaced by a single vertex prior to the mesh refinement step.
plot.delay

On Linux (and Mac if appropriate X11 libraries are installed), specifying a numeric value activates a rudimentary plotting system in the underlying fmesher program, showing the triangulation algorithm at work.
data.dir

Where to store the fmesher data files. Defaults to tempdir() if keep is FALSE, otherwise "inla.mesh.data".
keep

TRUE if the data files should be kept in data.dir or deleted afterwards. Defaults to true if data.dir is specified, otherwise false. Warning: If keep is false, data.dir and its contents will be deleted (unless set to tempdir()).
timings

If TRUE, obtain timings for the mesh construction.
quality.spec

List of vectors of per vertex max.edge target specification for each location in loc, boundary/interior (segm), and lattice. Only used if refining the mesh.
crs

An optional CRS or inla.CRS object
...

Optional parameters passed on to inla.mesh.create.

Value

An inla.mesh object.

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.2d(), inla.mesh.1d(), inla.mesh.segment(), inla.mesh.lattice(), inla.mesh.query()

Examples

loc <- matrix(runif(10 * 2), 10, 2)

mesh <- inla.delaunay(loc)
plot(mesh)

mesh <- inla.mesh.create(loc,
  interior = inla.mesh.segment(idx = 1:2),
  extend = TRUE,
  refine = list(max.edge = 0.1)
)
plot(mesh)

loc2 <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), 4, 2)
mesh2 <- inla.mesh.create(
  loc = loc,
  boundary = inla.mesh.segment(loc2),
  interior = inla.mesh.segment(idx = 1:2),
  quality.spec = list(segm = 0.2, loc = 0.05),
  refine = list(min.angle = 26)
)
plot(mesh2)

Directional derivative matrices for functions on meshes.

Description

[Deprecated] Use fmesher::fm_basis() instead.

Calculates directional derivative matrices for functions on inla.mesh() objects.

Usage

inla.mesh.deriv(mesh, loc)

Arguments

mesh

An inla.mesh() object.
loc

Coordinates where the derivatives should be evaluated.

Value

A

The projection matrix, ⁠u(loc_i)=sum_j A_ij w_i⁠
dx, dy, dz

Derivative weight matrices, ⁠du/dx(loc_i)=sum_j dx_ij w_i⁠, etc.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Finite element matrices

Description

[Deprecated] Use fmesher::fm_fem() instead.

Constructs finite element matrices for inla.mesh() and inla.mesh.1d() objects.

Usage

inla.mesh.fem(mesh, order = 2)

Arguments

mesh

An inla.mesh() or inla.mesh.1d() object.
order

The model order.

Value

A list of sparse matrices based on basis functions psi_i:

c0

⁠c0[i,j] = < psi_i, 1 >⁠
c1

⁠c1[i,j] = < psi_i, psi_j >⁠
g1

⁠g1[i,j] = < grad psi_i, grad psi_j >⁠
g2

g2 = g1 * c0^-1 * g1
gk

gk = g1 * (c0^-1 * g1)^(k-1), up to and including k=order

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.1d.fem()

Lattice grids for inla.mesh

Description

[Deprecated] Use fmesher::fm_lattice_2d() instead.

Construct a lattice grid for inla.mesh()

Usage

inla.mesh.lattice(
  x = seq(0, 1, length.out = 2),
  y = seq(0, 1, length.out = 2),
  z = NULL,
  dims = if (is.matrix(x)) {
     dim(x)
 } else {
     c(length(x), length(y))
 },
  units = NULL,
  crs = NULL
)

Arguments

x

vector or grid matrix of x-values
y

vector of grid matrix of y-values
z

if x is a matrix, a grid matrix of z-values
dims

the size of the grid, length 2 vector
units

One of c("default", "longlat", "longsinlat").
crs

An optional CRS or inla.CRS object

Value

An inla.mesh.lattice object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh()

Examples

lattice <- inla.mesh.lattice(seq(0, 1, length.out = 17), seq(0, 1, length.out = 10))

## Use the lattice "as-is", without refinement:
mesh <- inla.mesh.create(lattice = lattice, boundary = lattice$segm)
mesh <- inla.mesh.create(lattice = lattice, extend = FALSE)
plot(mesh)

## Refine the triangulation, with limits on triangle angles and edges:
mesh <- inla.mesh.create(
  lattice = lattice,
  refine = list(max.edge = 0.08),
  extend = FALSE
)
plot(mesh)

## Add an extension around the lattice, but maintain the lattice edges:
mesh <- inla.mesh.create(
  lattice = lattice,
  refine = list(max.edge = 0.08),
  interior = lattice$segm
)
plot(mesh)

## Only add extension:
mesh <- inla.mesh.create(lattice = lattice, refine = list(max.edge = 0.08))
plot(mesh)

Coordinate mappings for inla.mesh projections.

Description

[Deprecated] Use fmesher::fm_mesh_2d_map() instead.

Calculates coordinate mappings for inla.mesh projections.

Usage

inla.mesh.map.lim(
  loc = NULL,
  projection = c("default", "longlat", "longsinlat", "mollweide")
)

inla.mesh.map(
  loc,
  projection = c("default", "longlat", "longsinlat", "mollweide"),
  inverse = TRUE
)

Arguments

loc

Coordinates to be mapped.
projection

The projection type.
inverse

If TRUE, loc are map coordinates and coordinates in the mesh domain are calculated. If FALSE, loc are coordinates in the mesh domain and the forward map projection is calculated.

Value

For inla.mesh.map.lim, a list:

xlim

X axis limits in the map domain
ylim

Y axis limits in the map domain

No attempt is made to find minimal limits for partial spherical domains.

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.project()

Methods for projecting to/from an inla.mesh

Description

[Deprecated] Use fmesher::fm_evaluate() and fmesher::fm_evaluator() instead.

Calculate a lattice projection to/from an inla.mesh().

The call inla.mesh.project(mesh, loc, field=..., ...), is a shortcut to inla.mesh.project(inla.mesh.projector(mesh, loc), field).

Usage

inla.mesh.project(...)

inla.mesh.projector(...)

Arguments

...

Arguments passed on to fmesher::fm_evaluate() and fmesher::fm_evaluator().

Value

For inla.mesh.project(mesh, ...), a list with projection information. For inla.mesh.projector(mesh, ...), an inla.mesh.projector object. For inla.mesh.project(projector, field, ...), a field projected from the mesh onto the locations given by the projector object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh(), inla.mesh.1d(), inla.mesh.lattice()

Examples

n <- 20
loc <- matrix(runif(n * 2), n, 2)
mesh <- inla.mesh.create(loc, refine = list(max.edge = 0.05))
proj <- inla.mesh.projector(mesh)
field <- cos(mesh$loc[, 1] * 2 * pi * 3) * sin(mesh$loc[, 2] * 2 * pi * 7)
image(proj$x, proj$y, inla.mesh.project(proj, field))

if (require(rgl)) {
  plot(mesh, rgl = TRUE, col = field, draw.edges = FALSE, draw.vertices = FALSE)
}

High-quality triangulations

Description

Query information about an inla.mesh object.

Usage

inla.mesh.query(mesh, ...)

Arguments

mesh

An inla.mesh object.
...

Query arguments.

  • tt.neighbours Compute neighbour triangles for triangles; list of vectors: list(triangles, orders)

  • vt.neighbours Compute neighbour triangles for vertices; list of vectors: list(vertices, orders)

Value

A list of query results.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.create(), inla.mesh.segment(), inla.mesh.lattice()

Examples

loc <- matrix(c(0.1, 0.15), 1, 2)
lattice <- inla.mesh.lattice(
  seq(0, 1, length.out = 10),
  seq(0, 1, length.out = 10)
)
mesh <- inla.mesh.create(loc = loc, lattice = lattice, extend = FALSE)

vt <- which(inla.mesh.query(mesh,
  vt.neighbours = list(
    mesh$idx$loc,
    4:6
  )
)$vt.neighbours)

mesh2 <- inla.mesh.create(mesh$loc,
  tv = mesh$graph$tv[vt, , drop = FALSE],
  refine = FALSE, extend = FALSE
)

Constraint segments for inla.mesh

Description

[Deprecated] Use fmesher::fm_segm() instead.

Constructs inla.mesh.segment objects that can be used to specify boundary and interior constraint edges in calls to inla.mesh().

Usage

inla.mesh.segment(...)

inla.contour.segment(...)

Arguments

...

Parameters passed on to fmesher::fm_segm() and other replacement fmesher functions.

Value

An fm_segm object.

Functions

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.create(), inla.mesh.2d()

Examples

require("fmesher")
## Create a square boundary and a diagonal interior segment
loc.bnd <- matrix(c(0, 0, 1, 0, 1, 1, 0, 1), 4, 2, byrow = TRUE)
loc.int <- matrix(c(0.9, 0.1, 0.1, 0.6), 2, 2, byrow = TRUE)
segm.bnd <- fm_segm(loc.bnd)
segm.int <- fm_segm(loc.int, is.bnd = FALSE)

## Points to be meshed
loc <- matrix(runif(10 * 2), 10, 2) * 0.9 + 0.05
mesh <- fm_rcdt_2d_inla(loc,
  boundary = segm.bnd,
  interior = segm.int,
  refine = list()
)
plot(mesh)

mesh <- fm_rcdt_2d_inla(loc, interior = fm_segm_join(segm.bnd, segm.int))
plot(mesh)

Valid models in INLA

Description

This page describe the models implemented in inla, divided into sections: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.

Usage

inla.models()

Value

Valid sections are: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.

'latent'

Valid models in this section are:

Model 'linear'.
Properties:
doc =

⁠Alternative interface to an fixed effect⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠linear⁠

Number of hyperparmeters is 0.

Model 'iid'.
Properties:
doc =

⁠Gaussian random effects in dim=1⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠indep⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠1001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'mec'.
Properties:
doc =

⁠Classical measurement error model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠mec⁠

Number of hyperparmeters is 4.

Hyperparameter 'theta1'
hyperid =

⁠2001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

prior =

⁠gaussian⁠

param =

⁠1 0.001⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠2002⁠

name =

⁠prec.u⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 1e-04⁠

initial =

⁠9.21034037197618⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠2003⁠

name =

⁠mean.x⁠

short.name =

⁠mu.x⁠

prior =

⁠gaussian⁠

param =

⁠0 1e-04⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠2004⁠

name =

⁠prec.x⁠

short.name =

⁠prec.x⁠

prior =

⁠loggamma⁠

param =

⁠1 10000⁠

initial =

⁠-9.21034037197618⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'meb'.
Properties:
doc =

⁠Berkson measurement error model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠meb⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠3001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

prior =

⁠gaussian⁠

param =

⁠1 0.001⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠3002⁠

name =

⁠prec.u⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 1e-04⁠

initial =

⁠6.90775527898214⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'rgeneric'.
Properties:
doc =

⁠Generic latent model specified using R⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠rgeneric⁠

Number of hyperparmeters is 0.

Model 'cgeneric'.
Properties:
doc =

⁠Generic latent model specified using C⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠rgeneric⁠

Number of hyperparmeters is 0.

Model 'rw1'.
Properties:
doc =

⁠Random walk of order 1⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

min.diff =

⁠1e-06⁠

pdf =

⁠rw1⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠4001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'rw2'.
Properties:
doc =

⁠Random walk of order 2⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

min.diff =

⁠1e-04⁠

pdf =

⁠rw2⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠5001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'crw2'.
Properties:
doc =

⁠Exact solution to the random walk of order 2⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠2⁠

aug.constr =

⁠1⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

min.diff =

⁠1e-04⁠

pdf =

⁠crw2⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠6001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'seasonal'.
Properties:
doc =

⁠Seasonal model for time series⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠seasonal⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠7001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'besag'.
Properties:
doc =

⁠The Besag area model (CAR-model)⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠besag⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠8001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'besag2'.
Properties:
doc =

⁠The shared Besag model⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2⁠

n.div.by =

⁠2⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠besag2⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠9001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠9002⁠

name =

⁠scaling parameter⁠

short.name =

⁠a⁠

prior =

⁠loggamma⁠

param =

⁠10 10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'bym'.
Properties:
doc =

⁠The BYM-model (Besag-York-Mollier model)⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠2⁠

aug.constr =

⁠2⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠bym⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠10001⁠

name =

⁠log unstructured precision⁠

short.name =

⁠prec.unstruct⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-04⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠10002⁠

name =

⁠log spatial precision⁠

short.name =

⁠prec.spatial⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-04⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'bym2'.
Properties:
doc =

⁠The BYM-model with the PC priors⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠2⁠

aug.constr =

⁠2⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠bym2⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠11001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.prec⁠

param =

⁠1 0.01⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠11002⁠

name =

⁠logit phi⁠

short.name =

⁠phi⁠

prior =

⁠pc⁠

param =

⁠0.5 0.5⁠

initial =

⁠-3⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'besagproper'.
Properties:
doc =

⁠A proper version of the Besag model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠besagproper⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠12001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-04⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠12002⁠

name =

⁠log diagonal⁠

short.name =

⁠diag⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'besagproper2'.
Properties:
doc =

⁠An alternative proper version of the Besag model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠besagproper2⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠13001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-04⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠13002⁠

name =

⁠logit lambda⁠

short.name =

⁠lambda⁠

prior =

⁠gaussian⁠

param =

⁠0 0.45⁠

initial =

⁠3⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'fgn'.
Properties:
doc =

⁠Fractional Gaussian noise model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠5⁠

aug.constr =

⁠1⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

order.default =

⁠4⁠

order.defined =

⁠3 4⁠

pdf =

⁠fgn⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠13101⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.prec⁠

param =

⁠3 0.01⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠13102⁠

name =

⁠logit H⁠

short.name =

⁠H⁠

prior =

⁠pcfgnh⁠

param =

⁠0.9 0.1⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log((2 * x - 1) / (2 * (1 - x)))⁠

from.theta =

⁠function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))⁠

Model 'fgn2'.
Properties:
doc =

⁠Fractional Gaussian noise model (alt 2)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠4⁠

aug.constr =

⁠1⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

order.default =

⁠4⁠

order.defined =

⁠3 4⁠

pdf =

⁠fgn⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠13111⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.prec⁠

param =

⁠3 0.01⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠13112⁠

name =

⁠logit H⁠

short.name =

⁠H⁠

prior =

⁠pcfgnh⁠

param =

⁠0.9 0.1⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log((2 * x - 1) / (2 * (1 - x)))⁠

from.theta =

⁠function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))⁠

Model 'ar1'.
Properties:
doc =

⁠Auto-regressive model of order 1 (AR(1))⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠ar1⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠14001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠14002⁠

name =

⁠logit lag one correlation⁠

short.name =

⁠rho⁠

prior =

⁠normal⁠

param =

⁠0 0.15⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta3'
hyperid =

⁠14003⁠

name =

⁠mean⁠

short.name =

⁠mean⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'ar1c'.
Properties:
doc =

⁠Auto-regressive model of order 1 w/covariates⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠ar1c⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠14101⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.prec⁠

param =

⁠1 0.01⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠14102⁠

name =

⁠logit lag one correlation⁠

short.name =

⁠rho⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.5⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'ar'.
Properties:
doc =

⁠Auto-regressive model of order p (AR(p))⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠ar⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠15001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.prec⁠

param =

⁠3 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠15002⁠

name =

⁠pacf1⁠

short.name =

⁠pacf1⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.5⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta3'
hyperid =

⁠15003⁠

name =

⁠pacf2⁠

short.name =

⁠pacf2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.4⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta4'
hyperid =

⁠15004⁠

name =

⁠pacf3⁠

short.name =

⁠pacf3⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.3⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta5'
hyperid =

⁠15005⁠

name =

⁠pacf4⁠

short.name =

⁠pacf4⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.2⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta6'
hyperid =

⁠15006⁠

name =

⁠pacf5⁠

short.name =

⁠pacf5⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta7'
hyperid =

⁠15007⁠

name =

⁠pacf6⁠

short.name =

⁠pacf6⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta8'
hyperid =

⁠15008⁠

name =

⁠pacf7⁠

short.name =

⁠pacf7⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta9'
hyperid =

⁠15009⁠

name =

⁠pacf8⁠

short.name =

⁠pacf8⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta10'
hyperid =

⁠15010⁠

name =

⁠pacf9⁠

short.name =

⁠pacf9⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta11'
hyperid =

⁠15011⁠

name =

⁠pacf10⁠

short.name =

⁠pacf10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'ou'.
Properties:
doc =

⁠The Ornstein-Uhlenbeck process⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠ou⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠16001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠16002⁠

name =

⁠log phi⁠

short.name =

⁠phi⁠

prior =

⁠normal⁠

param =

⁠0 0.2⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'intslope'.
Properties:
doc =

⁠Intecept-slope model with Wishart-prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠intslope⁠

Number of hyperparmeters is 53.

Hyperparameter 'theta1'
hyperid =

⁠16101⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠wishart2d⁠

param =

⁠4 1 1 0⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠16102⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠16103⁠

name =

⁠logit correlation⁠

short.name =

⁠cor⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta4'
hyperid =

⁠16104⁠

name =

⁠gamma1⁠

short.name =

⁠g1⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠16105⁠

name =

⁠gamma2⁠

short.name =

⁠g2⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠16106⁠

name =

⁠gamma3⁠

short.name =

⁠g3⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠16107⁠

name =

⁠gamma4⁠

short.name =

⁠g4⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠16108⁠

name =

⁠gamma5⁠

short.name =

⁠g5⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠16109⁠

name =

⁠gamma6⁠

short.name =

⁠g6⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠16110⁠

name =

⁠gamma7⁠

short.name =

⁠g7⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠16111⁠

name =

⁠gamma8⁠

short.name =

⁠g8⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠16112⁠

name =

⁠gamma9⁠

short.name =

⁠g9⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠16113⁠

name =

⁠gamma10⁠

short.name =

⁠g10⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠16114⁠

name =

⁠gamma11⁠

short.name =

⁠g11⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠16115⁠

name =

⁠gamma12⁠

short.name =

⁠g12⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠16116⁠

name =

⁠gamma13⁠

short.name =

⁠g13⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta17'
hyperid =

⁠16117⁠

name =

⁠gamma14⁠

short.name =

⁠g14⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta18'
hyperid =

⁠16118⁠

name =

⁠gamma15⁠

short.name =

⁠g15⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta19'
hyperid =

⁠16119⁠

name =

⁠gamma16⁠

short.name =

⁠g16⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta20'
hyperid =

⁠16120⁠

name =

⁠gamma17⁠

short.name =

⁠g17⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta21'
hyperid =

⁠16121⁠

name =

⁠gamma18⁠

short.name =

⁠g18⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta22'
hyperid =

⁠16122⁠

name =

⁠gamma19⁠

short.name =

⁠g19⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta23'
hyperid =

⁠16123⁠

name =

⁠gamma20⁠

short.name =

⁠g20⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta24'
hyperid =

⁠16124⁠

name =

⁠gamma21⁠

short.name =

⁠g21⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta25'
hyperid =

⁠16125⁠

name =

⁠gamma22⁠

short.name =

⁠g22⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta26'
hyperid =

⁠16126⁠

name =

⁠gamma23⁠

short.name =

⁠g23⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta27'
hyperid =

⁠16127⁠

name =

⁠gamma24⁠

short.name =

⁠g24⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta28'
hyperid =

⁠16128⁠

name =

⁠gamma25⁠

short.name =

⁠g25⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta29'
hyperid =

⁠16129⁠

name =

⁠gamma26⁠

short.name =

⁠g26⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta30'
hyperid =

⁠16130⁠

name =

⁠gamma27⁠

short.name =

⁠g27⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta31'
hyperid =

⁠16131⁠

name =

⁠gamma28⁠

short.name =

⁠g28⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta32'
hyperid =

⁠16132⁠

name =

⁠gamma29⁠

short.name =

⁠g29⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta33'
hyperid =

⁠16133⁠

name =

⁠gamma30⁠

short.name =

⁠g30⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta34'
hyperid =

⁠16134⁠

name =

⁠gamma31⁠

short.name =

⁠g31⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta35'
hyperid =

⁠16135⁠

name =

⁠gamma32⁠

short.name =

⁠g32⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta36'
hyperid =

⁠16136⁠

name =

⁠gamma33⁠

short.name =

⁠g33⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta37'
hyperid =

⁠16137⁠

name =

⁠gamma34⁠

short.name =

⁠g34⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta38'
hyperid =

⁠16138⁠

name =

⁠gamma35⁠

short.name =

⁠g35⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta39'
hyperid =

⁠16139⁠

name =

⁠gamma36⁠

short.name =

⁠g36⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta40'
hyperid =

⁠16140⁠

name =

⁠gamma37⁠

short.name =

⁠g37⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta41'
hyperid =

⁠16141⁠

name =

⁠gamma38⁠

short.name =

⁠g38⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta42'
hyperid =

⁠16142⁠

name =

⁠gamma39⁠

short.name =

⁠g39⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta43'
hyperid =

⁠16143⁠

name =

⁠gamma40⁠

short.name =

⁠g40⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta44'
hyperid =

⁠16144⁠

name =

⁠gamma41⁠

short.name =

⁠g41⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta45'
hyperid =

⁠16145⁠

name =

⁠gamma42⁠

short.name =

⁠g42⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta46'
hyperid =

⁠16146⁠

name =

⁠gamma43⁠

short.name =

⁠g43⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta47'
hyperid =

⁠16147⁠

name =

⁠gamma44⁠

short.name =

⁠g44⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta48'
hyperid =

⁠16148⁠

name =

⁠gamma45⁠

short.name =

⁠g45⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta49'
hyperid =

⁠16149⁠

name =

⁠gamma46⁠

short.name =

⁠g46⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta50'
hyperid =

⁠16150⁠

name =

⁠gamma47⁠

short.name =

⁠g47⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta51'
hyperid =

⁠16151⁠

name =

⁠gamma48⁠

short.name =

⁠g48⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta52'
hyperid =

⁠16152⁠

name =

⁠gamma49⁠

short.name =

⁠g49⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta53'
hyperid =

⁠16153⁠

name =

⁠gamma50⁠

short.name =

⁠g50⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 36⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'generic'.
Properties:
doc =

⁠A generic model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠generic0⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠17001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'generic0'.
Properties:
doc =

⁠A generic model (type 0)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠generic0⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠18001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'generic1'.
Properties:
doc =

⁠A generic model (type 1)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠generic1⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠19001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠19002⁠

name =

⁠beta⁠

short.name =

⁠beta⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 0.1⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'generic2'.
Properties:
doc =

⁠A generic model (type 2)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠2⁠

aug.constr =

⁠2⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠generic2⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠20001⁠

name =

⁠log precision cmatrix⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠20002⁠

name =

⁠log precision random⁠

short.name =

⁠prec.random⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.001⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'generic3'.
Properties:
doc =

⁠A generic model (type 3)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠generic3⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠21001⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠21002⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠21003⁠

name =

⁠log precision3⁠

short.name =

⁠prec3⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠21004⁠

name =

⁠log precision4⁠

short.name =

⁠prec4⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta5'
hyperid =

⁠21005⁠

name =

⁠log precision5⁠

short.name =

⁠prec5⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta6'
hyperid =

⁠21006⁠

name =

⁠log precision6⁠

short.name =

⁠prec6⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta7'
hyperid =

⁠21007⁠

name =

⁠log precision7⁠

short.name =

⁠prec7⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta8'
hyperid =

⁠21008⁠

name =

⁠log precision8⁠

short.name =

⁠prec8⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta9'
hyperid =

⁠21009⁠

name =

⁠log precision9⁠

short.name =

⁠prec9⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta10'
hyperid =

⁠21010⁠

name =

⁠log precision10⁠

short.name =

⁠prec10⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta11'
hyperid =

⁠21011⁠

name =

⁠log precision common⁠

short.name =

⁠prec.common⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'spde'.
Properties:
doc =

⁠A SPDE model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠spde⁠

Number of hyperparmeters is 4.

Hyperparameter 'theta1'
hyperid =

⁠22001⁠

name =

⁠theta.T⁠

short.name =

⁠T⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠22002⁠

name =

⁠theta.K⁠

short.name =

⁠K⁠

initial =

⁠-2⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠22003⁠

name =

⁠theta.KT⁠

short.name =

⁠KT⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠22004⁠

name =

⁠theta.OC⁠

short.name =

⁠OC⁠

initial =

⁠-20⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠0 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'spde2'.
Properties:
doc =

⁠A SPDE2 model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠spde2⁠

Number of hyperparmeters is 100.

Hyperparameter 'theta1'
hyperid =

⁠23001⁠

name =

⁠theta1⁠

short.name =

⁠t1⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠mvnorm⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠23002⁠

name =

⁠theta2⁠

short.name =

⁠t2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠23003⁠

name =

⁠theta3⁠

short.name =

⁠t3⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠23004⁠

name =

⁠theta4⁠

short.name =

⁠t4⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠23005⁠

name =

⁠theta5⁠

short.name =

⁠t5⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠23006⁠

name =

⁠theta6⁠

short.name =

⁠t6⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠23007⁠

name =

⁠theta7⁠

short.name =

⁠t7⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠23008⁠

name =

⁠theta8⁠

short.name =

⁠t8⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠23009⁠

name =

⁠theta9⁠

short.name =

⁠t9⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠23010⁠

name =

⁠theta10⁠

short.name =

⁠t10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠23011⁠

name =

⁠theta11⁠

short.name =

⁠t11⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠23012⁠

name =

⁠theta12⁠

short.name =

⁠t12⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠23013⁠

name =

⁠theta13⁠

short.name =

⁠t13⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠23014⁠

name =

⁠theta14⁠

short.name =

⁠t14⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠23015⁠

name =

⁠theta15⁠

short.name =

⁠t15⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠23016⁠

name =

⁠theta16⁠

short.name =

⁠t16⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta17'
hyperid =

⁠23017⁠

name =

⁠theta17⁠

short.name =

⁠t17⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta18'
hyperid =

⁠23018⁠

name =

⁠theta18⁠

short.name =

⁠t18⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta19'
hyperid =

⁠23019⁠

name =

⁠theta19⁠

short.name =

⁠t19⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta20'
hyperid =

⁠23020⁠

name =

⁠theta20⁠

short.name =

⁠t20⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta21'
hyperid =

⁠23021⁠

name =

⁠theta21⁠

short.name =

⁠t21⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta22'
hyperid =

⁠23022⁠

name =

⁠theta22⁠

short.name =

⁠t22⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta23'
hyperid =

⁠23023⁠

name =

⁠theta23⁠

short.name =

⁠t23⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta24'
hyperid =

⁠23024⁠

name =

⁠theta24⁠

short.name =

⁠t24⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta25'
hyperid =

⁠23025⁠

name =

⁠theta25⁠

short.name =

⁠t25⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta26'
hyperid =

⁠23026⁠

name =

⁠theta26⁠

short.name =

⁠t26⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta27'
hyperid =

⁠23027⁠

name =

⁠theta27⁠

short.name =

⁠t27⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta28'
hyperid =

⁠23028⁠

name =

⁠theta28⁠

short.name =

⁠t28⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta29'
hyperid =

⁠23029⁠

name =

⁠theta29⁠

short.name =

⁠t29⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta30'
hyperid =

⁠23030⁠

name =

⁠theta30⁠

short.name =

⁠t30⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta31'
hyperid =

⁠23031⁠

name =

⁠theta31⁠

short.name =

⁠t31⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta32'
hyperid =

⁠23032⁠

name =

⁠theta32⁠

short.name =

⁠t32⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta33'
hyperid =

⁠23033⁠

name =

⁠theta33⁠

short.name =

⁠t33⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta34'
hyperid =

⁠23034⁠

name =

⁠theta34⁠

short.name =

⁠t34⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta35'
hyperid =

⁠23035⁠

name =

⁠theta35⁠

short.name =

⁠t35⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta36'
hyperid =

⁠23036⁠

name =

⁠theta36⁠

short.name =

⁠t36⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta37'
hyperid =

⁠23037⁠

name =

⁠theta37⁠

short.name =

⁠t37⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta38'
hyperid =

⁠23038⁠

name =

⁠theta38⁠

short.name =

⁠t38⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta39'
hyperid =

⁠23039⁠

name =

⁠theta39⁠

short.name =

⁠t39⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta40'
hyperid =

⁠23040⁠

name =

⁠theta40⁠

short.name =

⁠t40⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta41'
hyperid =

⁠23041⁠

name =

⁠theta41⁠

short.name =

⁠t41⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta42'
hyperid =

⁠23042⁠

name =

⁠theta42⁠

short.name =

⁠t42⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta43'
hyperid =

⁠23043⁠

name =

⁠theta43⁠

short.name =

⁠t43⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta44'
hyperid =

⁠23044⁠

name =

⁠theta44⁠

short.name =

⁠t44⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta45'
hyperid =

⁠23045⁠

name =

⁠theta45⁠

short.name =

⁠t45⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta46'
hyperid =

⁠23046⁠

name =

⁠theta46⁠

short.name =

⁠t46⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta47'
hyperid =

⁠23047⁠

name =

⁠theta47⁠

short.name =

⁠t47⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta48'
hyperid =

⁠23048⁠

name =

⁠theta48⁠

short.name =

⁠t48⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta49'
hyperid =

⁠23049⁠

name =

⁠theta49⁠

short.name =

⁠t49⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta50'
hyperid =

⁠23050⁠

name =

⁠theta50⁠

short.name =

⁠t50⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta51'
hyperid =

⁠23051⁠

name =

⁠theta51⁠

short.name =

⁠t51⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta52'
hyperid =

⁠23052⁠

name =

⁠theta52⁠

short.name =

⁠t52⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta53'
hyperid =

⁠23053⁠

name =

⁠theta53⁠

short.name =

⁠t53⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta54'
hyperid =

⁠23054⁠

name =

⁠theta54⁠

short.name =

⁠t54⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta55'
hyperid =

⁠23055⁠

name =

⁠theta55⁠

short.name =

⁠t55⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta56'
hyperid =

⁠23056⁠

name =

⁠theta56⁠

short.name =

⁠t56⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta57'
hyperid =

⁠23057⁠

name =

⁠theta57⁠

short.name =

⁠t57⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta58'
hyperid =

⁠23058⁠

name =

⁠theta58⁠

short.name =

⁠t58⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta59'
hyperid =

⁠23059⁠

name =

⁠theta59⁠

short.name =

⁠t59⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta60'
hyperid =

⁠23060⁠

name =

⁠theta60⁠

short.name =

⁠t60⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta61'
hyperid =

⁠23061⁠

name =

⁠theta61⁠

short.name =

⁠t61⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta62'
hyperid =

⁠23062⁠

name =

⁠theta62⁠

short.name =

⁠t62⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta63'
hyperid =

⁠23063⁠

name =

⁠theta63⁠

short.name =

⁠t63⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta64'
hyperid =

⁠23064⁠

name =

⁠theta64⁠

short.name =

⁠t64⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta65'
hyperid =

⁠23065⁠

name =

⁠theta65⁠

short.name =

⁠t65⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta66'
hyperid =

⁠23066⁠

name =

⁠theta66⁠

short.name =

⁠t66⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta67'
hyperid =

⁠23067⁠

name =

⁠theta67⁠

short.name =

⁠t67⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta68'
hyperid =

⁠23068⁠

name =

⁠theta68⁠

short.name =

⁠t68⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta69'
hyperid =

⁠23069⁠

name =

⁠theta69⁠

short.name =

⁠t69⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta70'
hyperid =

⁠23070⁠

name =

⁠theta70⁠

short.name =

⁠t70⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta71'
hyperid =

⁠23071⁠

name =

⁠theta71⁠

short.name =

⁠t71⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta72'
hyperid =

⁠23072⁠

name =

⁠theta72⁠

short.name =

⁠t72⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta73'
hyperid =

⁠23073⁠

name =

⁠theta73⁠

short.name =

⁠t73⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta74'
hyperid =

⁠23074⁠

name =

⁠theta74⁠

short.name =

⁠t74⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta75'
hyperid =

⁠23075⁠

name =

⁠theta75⁠

short.name =

⁠t75⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta76'
hyperid =

⁠23076⁠

name =

⁠theta76⁠

short.name =

⁠t76⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta77'
hyperid =

⁠23077⁠

name =

⁠theta77⁠

short.name =

⁠t77⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta78'
hyperid =

⁠23078⁠

name =

⁠theta78⁠

short.name =

⁠t78⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta79'
hyperid =

⁠23079⁠

name =

⁠theta79⁠

short.name =

⁠t79⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta80'
hyperid =

⁠23080⁠

name =

⁠theta80⁠

short.name =

⁠t80⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta81'
hyperid =

⁠23081⁠

name =

⁠theta81⁠

short.name =

⁠t81⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta82'
hyperid =

⁠23082⁠

name =

⁠theta82⁠

short.name =

⁠t82⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta83'
hyperid =

⁠23083⁠

name =

⁠theta83⁠

short.name =

⁠t83⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta84'
hyperid =

⁠23084⁠

name =

⁠theta84⁠

short.name =

⁠t84⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta85'
hyperid =

⁠23085⁠

name =

⁠theta85⁠

short.name =

⁠t85⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta86'
hyperid =

⁠23086⁠

name =

⁠theta86⁠

short.name =

⁠t86⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta87'
hyperid =

⁠23087⁠

name =

⁠theta87⁠

short.name =

⁠t87⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta88'
hyperid =

⁠23088⁠

name =

⁠theta88⁠

short.name =

⁠t88⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta89'
hyperid =

⁠23089⁠

name =

⁠theta89⁠

short.name =

⁠t89⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta90'
hyperid =

⁠23090⁠

name =

⁠theta90⁠

short.name =

⁠t90⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta91'
hyperid =

⁠23091⁠

name =

⁠theta91⁠

short.name =

⁠t91⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta92'
hyperid =

⁠23092⁠

name =

⁠theta92⁠

short.name =

⁠t92⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta93'
hyperid =

⁠23093⁠

name =

⁠theta93⁠

short.name =

⁠t93⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta94'
hyperid =

⁠23094⁠

name =

⁠theta94⁠

short.name =

⁠t94⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta95'
hyperid =

⁠23095⁠

name =

⁠theta95⁠

short.name =

⁠t95⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta96'
hyperid =

⁠23096⁠

name =

⁠theta96⁠

short.name =

⁠t96⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta97'
hyperid =

⁠23097⁠

name =

⁠theta97⁠

short.name =

⁠t97⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta98'
hyperid =

⁠23098⁠

name =

⁠theta98⁠

short.name =

⁠t98⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta99'
hyperid =

⁠23099⁠

name =

⁠theta99⁠

short.name =

⁠t99⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta100'
hyperid =

⁠23100⁠

name =

⁠theta100⁠

short.name =

⁠t100⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'spde3'.
Properties:
doc =

⁠A SPDE3 model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠spde3⁠

Number of hyperparmeters is 100.

Hyperparameter 'theta1'
hyperid =

⁠24001⁠

name =

⁠theta1⁠

short.name =

⁠t1⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠mvnorm⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠24002⁠

name =

⁠theta2⁠

short.name =

⁠t2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠24003⁠

name =

⁠theta3⁠

short.name =

⁠t3⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠24004⁠

name =

⁠theta4⁠

short.name =

⁠t4⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠24005⁠

name =

⁠theta5⁠

short.name =

⁠t5⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠24006⁠

name =

⁠theta6⁠

short.name =

⁠t6⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠24007⁠

name =

⁠theta7⁠

short.name =

⁠t7⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠24008⁠

name =

⁠theta8⁠

short.name =

⁠t8⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠24009⁠

name =

⁠theta9⁠

short.name =

⁠t9⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠24010⁠

name =

⁠theta10⁠

short.name =

⁠t10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠24011⁠

name =

⁠theta11⁠

short.name =

⁠t11⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠24012⁠

name =

⁠theta12⁠

short.name =

⁠t12⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠24013⁠

name =

⁠theta13⁠

short.name =

⁠t13⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠24014⁠

name =

⁠theta14⁠

short.name =

⁠t14⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠24015⁠

name =

⁠theta15⁠

short.name =

⁠t15⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠24016⁠

name =

⁠theta16⁠

short.name =

⁠t16⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta17'
hyperid =

⁠24017⁠

name =

⁠theta17⁠

short.name =

⁠t17⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta18'
hyperid =

⁠24018⁠

name =

⁠theta18⁠

short.name =

⁠t18⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta19'
hyperid =

⁠24019⁠

name =

⁠theta19⁠

short.name =

⁠t19⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta20'
hyperid =

⁠24020⁠

name =

⁠theta20⁠

short.name =

⁠t20⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta21'
hyperid =

⁠24021⁠

name =

⁠theta21⁠

short.name =

⁠t21⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta22'
hyperid =

⁠24022⁠

name =

⁠theta22⁠

short.name =

⁠t22⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta23'
hyperid =

⁠24023⁠

name =

⁠theta23⁠

short.name =

⁠t23⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta24'
hyperid =

⁠24024⁠

name =

⁠theta24⁠

short.name =

⁠t24⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta25'
hyperid =

⁠24025⁠

name =

⁠theta25⁠

short.name =

⁠t25⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta26'
hyperid =

⁠24026⁠

name =

⁠theta26⁠

short.name =

⁠t26⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta27'
hyperid =

⁠24027⁠

name =

⁠theta27⁠

short.name =

⁠t27⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta28'
hyperid =

⁠24028⁠

name =

⁠theta28⁠

short.name =

⁠t28⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta29'
hyperid =

⁠24029⁠

name =

⁠theta29⁠

short.name =

⁠t29⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta30'
hyperid =

⁠24030⁠

name =

⁠theta30⁠

short.name =

⁠t30⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta31'
hyperid =

⁠24031⁠

name =

⁠theta31⁠

short.name =

⁠t31⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta32'
hyperid =

⁠24032⁠

name =

⁠theta32⁠

short.name =

⁠t32⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta33'
hyperid =

⁠24033⁠

name =

⁠theta33⁠

short.name =

⁠t33⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta34'
hyperid =

⁠24034⁠

name =

⁠theta34⁠

short.name =

⁠t34⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta35'
hyperid =

⁠24035⁠

name =

⁠theta35⁠

short.name =

⁠t35⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta36'
hyperid =

⁠24036⁠

name =

⁠theta36⁠

short.name =

⁠t36⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta37'
hyperid =

⁠24037⁠

name =

⁠theta37⁠

short.name =

⁠t37⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta38'
hyperid =

⁠24038⁠

name =

⁠theta38⁠

short.name =

⁠t38⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta39'
hyperid =

⁠24039⁠

name =

⁠theta39⁠

short.name =

⁠t39⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta40'
hyperid =

⁠24040⁠

name =

⁠theta40⁠

short.name =

⁠t40⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta41'
hyperid =

⁠24041⁠

name =

⁠theta41⁠

short.name =

⁠t41⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta42'
hyperid =

⁠24042⁠

name =

⁠theta42⁠

short.name =

⁠t42⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta43'
hyperid =

⁠24043⁠

name =

⁠theta43⁠

short.name =

⁠t43⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta44'
hyperid =

⁠24044⁠

name =

⁠theta44⁠

short.name =

⁠t44⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta45'
hyperid =

⁠24045⁠

name =

⁠theta45⁠

short.name =

⁠t45⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta46'
hyperid =

⁠24046⁠

name =

⁠theta46⁠

short.name =

⁠t46⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta47'
hyperid =

⁠24047⁠

name =

⁠theta47⁠

short.name =

⁠t47⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta48'
hyperid =

⁠24048⁠

name =

⁠theta48⁠

short.name =

⁠t48⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta49'
hyperid =

⁠24049⁠

name =

⁠theta49⁠

short.name =

⁠t49⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta50'
hyperid =

⁠24050⁠

name =

⁠theta50⁠

short.name =

⁠t50⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta51'
hyperid =

⁠24051⁠

name =

⁠theta51⁠

short.name =

⁠t51⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta52'
hyperid =

⁠24052⁠

name =

⁠theta52⁠

short.name =

⁠t52⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta53'
hyperid =

⁠24053⁠

name =

⁠theta53⁠

short.name =

⁠t53⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta54'
hyperid =

⁠24054⁠

name =

⁠theta54⁠

short.name =

⁠t54⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta55'
hyperid =

⁠24055⁠

name =

⁠theta55⁠

short.name =

⁠t55⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta56'
hyperid =

⁠24056⁠

name =

⁠theta56⁠

short.name =

⁠t56⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta57'
hyperid =

⁠24057⁠

name =

⁠theta57⁠

short.name =

⁠t57⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta58'
hyperid =

⁠24058⁠

name =

⁠theta58⁠

short.name =

⁠t58⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta59'
hyperid =

⁠24059⁠

name =

⁠theta59⁠

short.name =

⁠t59⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta60'
hyperid =

⁠24060⁠

name =

⁠theta60⁠

short.name =

⁠t60⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta61'
hyperid =

⁠24061⁠

name =

⁠theta61⁠

short.name =

⁠t61⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta62'
hyperid =

⁠24062⁠

name =

⁠theta62⁠

short.name =

⁠t62⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta63'
hyperid =

⁠24063⁠

name =

⁠theta63⁠

short.name =

⁠t63⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta64'
hyperid =

⁠24064⁠

name =

⁠theta64⁠

short.name =

⁠t64⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta65'
hyperid =

⁠24065⁠

name =

⁠theta65⁠

short.name =

⁠t65⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta66'
hyperid =

⁠24066⁠

name =

⁠theta66⁠

short.name =

⁠t66⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta67'
hyperid =

⁠24067⁠

name =

⁠theta67⁠

short.name =

⁠t67⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta68'
hyperid =

⁠24068⁠

name =

⁠theta68⁠

short.name =

⁠t68⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta69'
hyperid =

⁠24069⁠

name =

⁠theta69⁠

short.name =

⁠t69⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta70'
hyperid =

⁠24070⁠

name =

⁠theta70⁠

short.name =

⁠t70⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta71'
hyperid =

⁠24071⁠

name =

⁠theta71⁠

short.name =

⁠t71⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta72'
hyperid =

⁠24072⁠

name =

⁠theta72⁠

short.name =

⁠t72⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta73'
hyperid =

⁠24073⁠

name =

⁠theta73⁠

short.name =

⁠t73⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta74'
hyperid =

⁠24074⁠

name =

⁠theta74⁠

short.name =

⁠t74⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta75'
hyperid =

⁠24075⁠

name =

⁠theta75⁠

short.name =

⁠t75⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta76'
hyperid =

⁠24076⁠

name =

⁠theta76⁠

short.name =

⁠t76⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta77'
hyperid =

⁠24077⁠

name =

⁠theta77⁠

short.name =

⁠t77⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta78'
hyperid =

⁠24078⁠

name =

⁠theta78⁠

short.name =

⁠t78⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta79'
hyperid =

⁠24079⁠

name =

⁠theta79⁠

short.name =

⁠t79⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta80'
hyperid =

⁠24080⁠

name =

⁠theta80⁠

short.name =

⁠t80⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta81'
hyperid =

⁠24081⁠

name =

⁠theta81⁠

short.name =

⁠t81⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta82'
hyperid =

⁠24082⁠

name =

⁠theta82⁠

short.name =

⁠t82⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta83'
hyperid =

⁠24083⁠

name =

⁠theta83⁠

short.name =

⁠t83⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta84'
hyperid =

⁠24084⁠

name =

⁠theta84⁠

short.name =

⁠t84⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta85'
hyperid =

⁠24085⁠

name =

⁠theta85⁠

short.name =

⁠t85⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta86'
hyperid =

⁠24086⁠

name =

⁠theta86⁠

short.name =

⁠t86⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta87'
hyperid =

⁠24087⁠

name =

⁠theta87⁠

short.name =

⁠t87⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta88'
hyperid =

⁠24088⁠

name =

⁠theta88⁠

short.name =

⁠t88⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta89'
hyperid =

⁠24089⁠

name =

⁠theta89⁠

short.name =

⁠t89⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta90'
hyperid =

⁠24090⁠

name =

⁠theta90⁠

short.name =

⁠t90⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta91'
hyperid =

⁠24091⁠

name =

⁠theta91⁠

short.name =

⁠t91⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta92'
hyperid =

⁠24092⁠

name =

⁠theta92⁠

short.name =

⁠t92⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta93'
hyperid =

⁠24093⁠

name =

⁠theta93⁠

short.name =

⁠t93⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta94'
hyperid =

⁠24094⁠

name =

⁠theta94⁠

short.name =

⁠t94⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta95'
hyperid =

⁠24095⁠

name =

⁠theta95⁠

short.name =

⁠t95⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta96'
hyperid =

⁠24096⁠

name =

⁠theta96⁠

short.name =

⁠t96⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta97'
hyperid =

⁠24097⁠

name =

⁠theta97⁠

short.name =

⁠t97⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta98'
hyperid =

⁠24098⁠

name =

⁠theta98⁠

short.name =

⁠t98⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta99'
hyperid =

⁠24099⁠

name =

⁠theta99⁠

short.name =

⁠t99⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta100'
hyperid =

⁠24100⁠

name =

⁠theta100⁠

short.name =

⁠t100⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'iid1d'.
Properties:
doc =

⁠Gaussian random effect in dim=1 with Wishart prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iid123d⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠25001⁠

name =

⁠precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠wishart1d⁠

param =

⁠2 1e-04⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'iid2d'.
Properties:
doc =

⁠Gaussian random effect in dim=2 with Wishart prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2⁠

n.div.by =

⁠2⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iid123d⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠26001⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠wishart2d⁠

param =

⁠4 1 1 0⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠26002⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠26003⁠

name =

⁠logit correlation⁠

short.name =

⁠cor⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'iid3d'.
Properties:
doc =

⁠Gaussian random effect in dim=3 with Wishart prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2 3⁠

n.div.by =

⁠3⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iid123d⁠

Number of hyperparmeters is 6.

Hyperparameter 'theta1'
hyperid =

⁠27001⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠wishart3d⁠

param =

⁠7 1 1 1 0 0 0⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠27002⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠27003⁠

name =

⁠log precision3⁠

short.name =

⁠prec3⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠27004⁠

name =

⁠logit correlation12⁠

short.name =

⁠cor12⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta5'
hyperid =

⁠27005⁠

name =

⁠logit correlation13⁠

short.name =

⁠cor13⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta6'
hyperid =

⁠27006⁠

name =

⁠logit correlation23⁠

short.name =

⁠cor23⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'iid4d'.
Properties:
doc =

⁠Gaussian random effect in dim=4 with Wishart prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2 3 4⁠

n.div.by =

⁠4⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iid123d⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠28001⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠wishart4d⁠

param =

⁠11 1 1 1 1 0 0 0 0 0 0⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠28002⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠28003⁠

name =

⁠log precision3⁠

short.name =

⁠prec3⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠28004⁠

name =

⁠log precision4⁠

short.name =

⁠prec4⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta5'
hyperid =

⁠28005⁠

name =

⁠logit correlation12⁠

short.name =

⁠cor12⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta6'
hyperid =

⁠28006⁠

name =

⁠logit correlation13⁠

short.name =

⁠cor13⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta7'
hyperid =

⁠28007⁠

name =

⁠logit correlation14⁠

short.name =

⁠cor14⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta8'
hyperid =

⁠28008⁠

name =

⁠logit correlation23⁠

short.name =

⁠cor23⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta9'
hyperid =

⁠28009⁠

name =

⁠logit correlation24⁠

short.name =

⁠cor24⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta10'
hyperid =

⁠28010⁠

name =

⁠logit correlation34⁠

short.name =

⁠cor34⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'iid5d'.
Properties:
doc =

⁠Gaussian random effect in dim=5 with Wishart prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2 3 4 5⁠

n.div.by =

⁠5⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iid123d⁠

Number of hyperparmeters is 15.

Hyperparameter 'theta1'
hyperid =

⁠29001⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠wishart5d⁠

param =

⁠16 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠29002⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠29003⁠

name =

⁠log precision3⁠

short.name =

⁠prec3⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠29004⁠

name =

⁠log precision4⁠

short.name =

⁠prec4⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta5'
hyperid =

⁠29005⁠

name =

⁠log precision5⁠

short.name =

⁠prec5⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta6'
hyperid =

⁠29006⁠

name =

⁠logit correlation12⁠

short.name =

⁠cor12⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta7'
hyperid =

⁠29007⁠

name =

⁠logit correlation13⁠

short.name =

⁠cor13⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta8'
hyperid =

⁠29008⁠

name =

⁠logit correlation14⁠

short.name =

⁠cor14⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta9'
hyperid =

⁠29009⁠

name =

⁠logit correlation15⁠

short.name =

⁠cor15⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta10'
hyperid =

⁠29010⁠

name =

⁠logit correlation23⁠

short.name =

⁠cor23⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta11'
hyperid =

⁠29011⁠

name =

⁠logit correlation24⁠

short.name =

⁠cor24⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta12'
hyperid =

⁠29012⁠

name =

⁠logit correlation25⁠

short.name =

⁠cor25⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta13'
hyperid =

⁠29013⁠

name =

⁠logit correlation34⁠

short.name =

⁠cor34⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta14'
hyperid =

⁠29014⁠

name =

⁠logit correlation35⁠

short.name =

⁠cor35⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta15'
hyperid =

⁠29015⁠

name =

⁠logit correlation45⁠

short.name =

⁠cor45⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'iidkd'.
Properties:
doc =

⁠Gaussian random effect in dim=k with Wishart prior⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24⁠

n.div.by =

⁠-1⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iidkd⁠

Number of hyperparmeters is 300.

Hyperparameter 'theta1'
hyperid =

⁠29101⁠

name =

⁠theta1⁠

short.name =

⁠theta1⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠wishartkd⁠

param =

⁠30 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠29102⁠

name =

⁠theta2⁠

short.name =

⁠theta2⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠29103⁠

name =

⁠theta3⁠

short.name =

⁠theta3⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠29104⁠

name =

⁠theta4⁠

short.name =

⁠theta4⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠29105⁠

name =

⁠theta5⁠

short.name =

⁠theta5⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠29106⁠

name =

⁠theta6⁠

short.name =

⁠theta6⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠29107⁠

name =

⁠theta7⁠

short.name =

⁠theta7⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠29108⁠

name =

⁠theta8⁠

short.name =

⁠theta8⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠29109⁠

name =

⁠theta9⁠

short.name =

⁠theta9⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠29110⁠

name =

⁠theta10⁠

short.name =

⁠theta10⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠29111⁠

name =

⁠theta11⁠

short.name =

⁠theta11⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠29112⁠

name =

⁠theta12⁠

short.name =

⁠theta12⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠29113⁠

name =

⁠theta13⁠

short.name =

⁠theta13⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠29114⁠

name =

⁠theta14⁠

short.name =

⁠theta14⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠29115⁠

name =

⁠theta15⁠

short.name =

⁠theta15⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠29116⁠

name =

⁠theta16⁠

short.name =

⁠theta16⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta17'
hyperid =

⁠29117⁠

name =

⁠theta17⁠

short.name =

⁠theta17⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta18'
hyperid =

⁠29118⁠

name =

⁠theta18⁠

short.name =

⁠theta18⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta19'
hyperid =

⁠29119⁠

name =

⁠theta19⁠

short.name =

⁠theta19⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta20'
hyperid =

⁠29120⁠

name =

⁠theta20⁠

short.name =

⁠theta20⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta21'
hyperid =

⁠29121⁠

name =

⁠theta21⁠

short.name =

⁠theta21⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta22'
hyperid =

⁠29122⁠

name =

⁠theta22⁠

short.name =

⁠theta22⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta23'
hyperid =

⁠29123⁠

name =

⁠theta23⁠

short.name =

⁠theta23⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta24'
hyperid =

⁠29124⁠

name =

⁠theta24⁠

short.name =

⁠theta24⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta25'
hyperid =

⁠29125⁠

name =

⁠theta25⁠

short.name =

⁠theta25⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta26'
hyperid =

⁠29126⁠

name =

⁠theta26⁠

short.name =

⁠theta26⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta27'
hyperid =

⁠29127⁠

name =

⁠theta27⁠

short.name =

⁠theta27⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta28'
hyperid =

⁠29128⁠

name =

⁠theta28⁠

short.name =

⁠theta28⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta29'
hyperid =

⁠29129⁠

name =

⁠theta29⁠

short.name =

⁠theta29⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta30'
hyperid =

⁠29130⁠

name =

⁠theta30⁠

short.name =

⁠theta30⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta31'
hyperid =

⁠29131⁠

name =

⁠theta31⁠

short.name =

⁠theta31⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta32'
hyperid =

⁠29132⁠

name =

⁠theta32⁠

short.name =

⁠theta32⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta33'
hyperid =

⁠29133⁠

name =

⁠theta33⁠

short.name =

⁠theta33⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta34'
hyperid =

⁠29134⁠

name =

⁠theta34⁠

short.name =

⁠theta34⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta35'
hyperid =

⁠29135⁠

name =

⁠theta35⁠

short.name =

⁠theta35⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta36'
hyperid =

⁠29136⁠

name =

⁠theta36⁠

short.name =

⁠theta36⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta37'
hyperid =

⁠29137⁠

name =

⁠theta37⁠

short.name =

⁠theta37⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta38'
hyperid =

⁠29138⁠

name =

⁠theta38⁠

short.name =

⁠theta38⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta39'
hyperid =

⁠29139⁠

name =

⁠theta39⁠

short.name =

⁠theta39⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta40'
hyperid =

⁠29140⁠

name =

⁠theta40⁠

short.name =

⁠theta40⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta41'
hyperid =

⁠29141⁠

name =

⁠theta41⁠

short.name =

⁠theta41⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta42'
hyperid =

⁠29142⁠

name =

⁠theta42⁠

short.name =

⁠theta42⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta43'
hyperid =

⁠29143⁠

name =

⁠theta43⁠

short.name =

⁠theta43⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta44'
hyperid =

⁠29144⁠

name =

⁠theta44⁠

short.name =

⁠theta44⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta45'
hyperid =

⁠29145⁠

name =

⁠theta45⁠

short.name =

⁠theta45⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta46'
hyperid =

⁠29146⁠

name =

⁠theta46⁠

short.name =

⁠theta46⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta47'
hyperid =

⁠29147⁠

name =

⁠theta47⁠

short.name =

⁠theta47⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta48'
hyperid =

⁠29148⁠

name =

⁠theta48⁠

short.name =

⁠theta48⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta49'
hyperid =

⁠29149⁠

name =

⁠theta49⁠

short.name =

⁠theta49⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta50'
hyperid =

⁠29150⁠

name =

⁠theta50⁠

short.name =

⁠theta50⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta51'
hyperid =

⁠29151⁠

name =

⁠theta51⁠

short.name =

⁠theta51⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta52'
hyperid =

⁠29152⁠

name =

⁠theta52⁠

short.name =

⁠theta52⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta53'
hyperid =

⁠29153⁠

name =

⁠theta53⁠

short.name =

⁠theta53⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta54'
hyperid =

⁠29154⁠

name =

⁠theta54⁠

short.name =

⁠theta54⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta55'
hyperid =

⁠29155⁠

name =

⁠theta55⁠

short.name =

⁠theta55⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta56'
hyperid =

⁠29156⁠

name =

⁠theta56⁠

short.name =

⁠theta56⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta57'
hyperid =

⁠29157⁠

name =

⁠theta57⁠

short.name =

⁠theta57⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta58'
hyperid =

⁠29158⁠

name =

⁠theta58⁠

short.name =

⁠theta58⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta59'
hyperid =

⁠29159⁠

name =

⁠theta59⁠

short.name =

⁠theta59⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta60'
hyperid =

⁠29160⁠

name =

⁠theta60⁠

short.name =

⁠theta60⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta61'
hyperid =

⁠29161⁠

name =

⁠theta61⁠

short.name =

⁠theta61⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta62'
hyperid =

⁠29162⁠

name =

⁠theta62⁠

short.name =

⁠theta62⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta63'
hyperid =

⁠29163⁠

name =

⁠theta63⁠

short.name =

⁠theta63⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta64'
hyperid =

⁠29164⁠

name =

⁠theta64⁠

short.name =

⁠theta64⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta65'
hyperid =

⁠29165⁠

name =

⁠theta65⁠

short.name =

⁠theta65⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta66'
hyperid =

⁠29166⁠

name =

⁠theta66⁠

short.name =

⁠theta66⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta67'
hyperid =

⁠29167⁠

name =

⁠theta67⁠

short.name =

⁠theta67⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta68'
hyperid =

⁠29168⁠

name =

⁠theta68⁠

short.name =

⁠theta68⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta69'
hyperid =

⁠29169⁠

name =

⁠theta69⁠

short.name =

⁠theta69⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta70'
hyperid =

⁠29170⁠

name =

⁠theta70⁠

short.name =

⁠theta70⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta71'
hyperid =

⁠29171⁠

name =

⁠theta71⁠

short.name =

⁠theta71⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta72'
hyperid =

⁠29172⁠

name =

⁠theta72⁠

short.name =

⁠theta72⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta73'
hyperid =

⁠29173⁠

name =

⁠theta73⁠

short.name =

⁠theta73⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta74'
hyperid =

⁠29174⁠

name =

⁠theta74⁠

short.name =

⁠theta74⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta75'
hyperid =

⁠29175⁠

name =

⁠theta75⁠

short.name =

⁠theta75⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta76'
hyperid =

⁠29176⁠

name =

⁠theta76⁠

short.name =

⁠theta76⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta77'
hyperid =

⁠29177⁠

name =

⁠theta77⁠

short.name =

⁠theta77⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta78'
hyperid =

⁠29178⁠

name =

⁠theta78⁠

short.name =

⁠theta78⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta79'
hyperid =

⁠29179⁠

name =

⁠theta79⁠

short.name =

⁠theta79⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta80'
hyperid =

⁠29180⁠

name =

⁠theta80⁠

short.name =

⁠theta80⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta81'
hyperid =

⁠29181⁠

name =

⁠theta81⁠

short.name =

⁠theta81⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta82'
hyperid =

⁠29182⁠

name =

⁠theta82⁠

short.name =

⁠theta82⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta83'
hyperid =

⁠29183⁠

name =

⁠theta83⁠

short.name =

⁠theta83⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta84'
hyperid =

⁠29184⁠

name =

⁠theta84⁠

short.name =

⁠theta84⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta85'
hyperid =

⁠29185⁠

name =

⁠theta85⁠

short.name =

⁠theta85⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta86'
hyperid =

⁠29186⁠

name =

⁠theta86⁠

short.name =

⁠theta86⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta87'
hyperid =

⁠29187⁠

name =

⁠theta87⁠

short.name =

⁠theta87⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta88'
hyperid =

⁠29188⁠

name =

⁠theta88⁠

short.name =

⁠theta88⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta89'
hyperid =

⁠29189⁠

name =

⁠theta89⁠

short.name =

⁠theta89⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta90'
hyperid =

⁠29190⁠

name =

⁠theta90⁠

short.name =

⁠theta90⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta91'
hyperid =

⁠29191⁠

name =

⁠theta91⁠

short.name =

⁠theta91⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta92'
hyperid =

⁠29192⁠

name =

⁠theta92⁠

short.name =

⁠theta92⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta93'
hyperid =

⁠29193⁠

name =

⁠theta93⁠

short.name =

⁠theta93⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta94'
hyperid =

⁠29194⁠

name =

⁠theta94⁠

short.name =

⁠theta94⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta95'
hyperid =

⁠29195⁠

name =

⁠theta95⁠

short.name =

⁠theta95⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta96'
hyperid =

⁠29196⁠

name =

⁠theta96⁠

short.name =

⁠theta96⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta97'
hyperid =

⁠29197⁠

name =

⁠theta97⁠

short.name =

⁠theta97⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta98'
hyperid =

⁠29198⁠

name =

⁠theta98⁠

short.name =

⁠theta98⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta99'
hyperid =

⁠29199⁠

name =

⁠theta99⁠

short.name =

⁠theta99⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta100'
hyperid =

⁠29200⁠

name =

⁠theta100⁠

short.name =

⁠theta100⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta101'
hyperid =

⁠29201⁠

name =

⁠theta101⁠

short.name =

⁠theta101⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta102'
hyperid =

⁠29202⁠

name =

⁠theta102⁠

short.name =

⁠theta102⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta103'
hyperid =

⁠29203⁠

name =

⁠theta103⁠

short.name =

⁠theta103⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta104'
hyperid =

⁠29204⁠

name =

⁠theta104⁠

short.name =

⁠theta104⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta105'
hyperid =

⁠29205⁠

name =

⁠theta105⁠

short.name =

⁠theta105⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta106'
hyperid =

⁠29206⁠

name =

⁠theta106⁠

short.name =

⁠theta106⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta107'
hyperid =

⁠29207⁠

name =

⁠theta107⁠

short.name =

⁠theta107⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta108'
hyperid =

⁠29208⁠

name =

⁠theta108⁠

short.name =

⁠theta108⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta109'
hyperid =

⁠29209⁠

name =

⁠theta109⁠

short.name =

⁠theta109⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta110'
hyperid =

⁠29210⁠

name =

⁠theta110⁠

short.name =

⁠theta110⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta111'
hyperid =

⁠29211⁠

name =

⁠theta111⁠

short.name =

⁠theta111⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta112'
hyperid =

⁠29212⁠

name =

⁠theta112⁠

short.name =

⁠theta112⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta113'
hyperid =

⁠29213⁠

name =

⁠theta113⁠

short.name =

⁠theta113⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta114'
hyperid =

⁠29214⁠

name =

⁠theta114⁠

short.name =

⁠theta114⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta115'
hyperid =

⁠29215⁠

name =

⁠theta115⁠

short.name =

⁠theta115⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta116'
hyperid =

⁠29216⁠

name =

⁠theta116⁠

short.name =

⁠theta116⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta117'
hyperid =

⁠29217⁠

name =

⁠theta117⁠

short.name =

⁠theta117⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta118'
hyperid =

⁠29218⁠

name =

⁠theta118⁠

short.name =

⁠theta118⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta119'
hyperid =

⁠29219⁠

name =

⁠theta119⁠

short.name =

⁠theta119⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta120'
hyperid =

⁠29220⁠

name =

⁠theta120⁠

short.name =

⁠theta120⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta121'
hyperid =

⁠29221⁠

name =

⁠theta121⁠

short.name =

⁠theta121⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta122'
hyperid =

⁠29222⁠

name =

⁠theta122⁠

short.name =

⁠theta122⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta123'
hyperid =

⁠29223⁠

name =

⁠theta123⁠

short.name =

⁠theta123⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta124'
hyperid =

⁠29224⁠

name =

⁠theta124⁠

short.name =

⁠theta124⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta125'
hyperid =

⁠29225⁠

name =

⁠theta125⁠

short.name =

⁠theta125⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta126'
hyperid =

⁠29226⁠

name =

⁠theta126⁠

short.name =

⁠theta126⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta127'
hyperid =

⁠29227⁠

name =

⁠theta127⁠

short.name =

⁠theta127⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta128'
hyperid =

⁠29228⁠

name =

⁠theta128⁠

short.name =

⁠theta128⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta129'
hyperid =

⁠29229⁠

name =

⁠theta129⁠

short.name =

⁠theta129⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta130'
hyperid =

⁠29230⁠

name =

⁠theta130⁠

short.name =

⁠theta130⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta131'
hyperid =

⁠29231⁠

name =

⁠theta131⁠

short.name =

⁠theta131⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta132'
hyperid =

⁠29232⁠

name =

⁠theta132⁠

short.name =

⁠theta132⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta133'
hyperid =

⁠29233⁠

name =

⁠theta133⁠

short.name =

⁠theta133⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta134'
hyperid =

⁠29234⁠

name =

⁠theta134⁠

short.name =

⁠theta134⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta135'
hyperid =

⁠29235⁠

name =

⁠theta135⁠

short.name =

⁠theta135⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta136'
hyperid =

⁠29236⁠

name =

⁠theta136⁠

short.name =

⁠theta136⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta137'
hyperid =

⁠29237⁠

name =

⁠theta137⁠

short.name =

⁠theta137⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta138'
hyperid =

⁠29238⁠

name =

⁠theta138⁠

short.name =

⁠theta138⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta139'
hyperid =

⁠29239⁠

name =

⁠theta139⁠

short.name =

⁠theta139⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta140'
hyperid =

⁠29240⁠

name =

⁠theta140⁠

short.name =

⁠theta140⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta141'
hyperid =

⁠29241⁠

name =

⁠theta141⁠

short.name =

⁠theta141⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta142'
hyperid =

⁠29242⁠

name =

⁠theta142⁠

short.name =

⁠theta142⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta143'
hyperid =

⁠29243⁠

name =

⁠theta143⁠

short.name =

⁠theta143⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta144'
hyperid =

⁠29244⁠

name =

⁠theta144⁠

short.name =

⁠theta144⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta145'
hyperid =

⁠29245⁠

name =

⁠theta145⁠

short.name =

⁠theta145⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta146'
hyperid =

⁠29246⁠

name =

⁠theta146⁠

short.name =

⁠theta146⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta147'
hyperid =

⁠29247⁠

name =

⁠theta147⁠

short.name =

⁠theta147⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta148'
hyperid =

⁠29248⁠

name =

⁠theta148⁠

short.name =

⁠theta148⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta149'
hyperid =

⁠29249⁠

name =

⁠theta149⁠

short.name =

⁠theta149⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta150'
hyperid =

⁠29250⁠

name =

⁠theta150⁠

short.name =

⁠theta150⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta151'
hyperid =

⁠29251⁠

name =

⁠theta151⁠

short.name =

⁠theta151⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta152'
hyperid =

⁠29252⁠

name =

⁠theta152⁠

short.name =

⁠theta152⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta153'
hyperid =

⁠29253⁠

name =

⁠theta153⁠

short.name =

⁠theta153⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta154'
hyperid =

⁠29254⁠

name =

⁠theta154⁠

short.name =

⁠theta154⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta155'
hyperid =

⁠29255⁠

name =

⁠theta155⁠

short.name =

⁠theta155⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta156'
hyperid =

⁠29256⁠

name =

⁠theta156⁠

short.name =

⁠theta156⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta157'
hyperid =

⁠29257⁠

name =

⁠theta157⁠

short.name =

⁠theta157⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta158'
hyperid =

⁠29258⁠

name =

⁠theta158⁠

short.name =

⁠theta158⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta159'
hyperid =

⁠29259⁠

name =

⁠theta159⁠

short.name =

⁠theta159⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta160'
hyperid =

⁠29260⁠

name =

⁠theta160⁠

short.name =

⁠theta160⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta161'
hyperid =

⁠29261⁠

name =

⁠theta161⁠

short.name =

⁠theta161⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta162'
hyperid =

⁠29262⁠

name =

⁠theta162⁠

short.name =

⁠theta162⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta163'
hyperid =

⁠29263⁠

name =

⁠theta163⁠

short.name =

⁠theta163⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta164'
hyperid =

⁠29264⁠

name =

⁠theta164⁠

short.name =

⁠theta164⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta165'
hyperid =

⁠29265⁠

name =

⁠theta165⁠

short.name =

⁠theta165⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta166'
hyperid =

⁠29266⁠

name =

⁠theta166⁠

short.name =

⁠theta166⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta167'
hyperid =

⁠29267⁠

name =

⁠theta167⁠

short.name =

⁠theta167⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta168'
hyperid =

⁠29268⁠

name =

⁠theta168⁠

short.name =

⁠theta168⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta169'
hyperid =

⁠29269⁠

name =

⁠theta169⁠

short.name =

⁠theta169⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta170'
hyperid =

⁠29270⁠

name =

⁠theta170⁠

short.name =

⁠theta170⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta171'
hyperid =

⁠29271⁠

name =

⁠theta171⁠

short.name =

⁠theta171⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta172'
hyperid =

⁠29272⁠

name =

⁠theta172⁠

short.name =

⁠theta172⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta173'
hyperid =

⁠29273⁠

name =

⁠theta173⁠

short.name =

⁠theta173⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta174'
hyperid =

⁠29274⁠

name =

⁠theta174⁠

short.name =

⁠theta174⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta175'
hyperid =

⁠29275⁠

name =

⁠theta175⁠

short.name =

⁠theta175⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta176'
hyperid =

⁠29276⁠

name =

⁠theta176⁠

short.name =

⁠theta176⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta177'
hyperid =

⁠29277⁠

name =

⁠theta177⁠

short.name =

⁠theta177⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta178'
hyperid =

⁠29278⁠

name =

⁠theta178⁠

short.name =

⁠theta178⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta179'
hyperid =

⁠29279⁠

name =

⁠theta179⁠

short.name =

⁠theta179⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta180'
hyperid =

⁠29280⁠

name =

⁠theta180⁠

short.name =

⁠theta180⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta181'
hyperid =

⁠29281⁠

name =

⁠theta181⁠

short.name =

⁠theta181⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta182'
hyperid =

⁠29282⁠

name =

⁠theta182⁠

short.name =

⁠theta182⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta183'
hyperid =

⁠29283⁠

name =

⁠theta183⁠

short.name =

⁠theta183⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta184'
hyperid =

⁠29284⁠

name =

⁠theta184⁠

short.name =

⁠theta184⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta185'
hyperid =

⁠29285⁠

name =

⁠theta185⁠

short.name =

⁠theta185⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta186'
hyperid =

⁠29286⁠

name =

⁠theta186⁠

short.name =

⁠theta186⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta187'
hyperid =

⁠29287⁠

name =

⁠theta187⁠

short.name =

⁠theta187⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta188'
hyperid =

⁠29288⁠

name =

⁠theta188⁠

short.name =

⁠theta188⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta189'
hyperid =

⁠29289⁠

name =

⁠theta189⁠

short.name =

⁠theta189⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta190'
hyperid =

⁠29290⁠

name =

⁠theta190⁠

short.name =

⁠theta190⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta191'
hyperid =

⁠29291⁠

name =

⁠theta191⁠

short.name =

⁠theta191⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta192'
hyperid =

⁠29292⁠

name =

⁠theta192⁠

short.name =

⁠theta192⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta193'
hyperid =

⁠29293⁠

name =

⁠theta193⁠

short.name =

⁠theta193⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta194'
hyperid =

⁠29294⁠

name =

⁠theta194⁠

short.name =

⁠theta194⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta195'
hyperid =

⁠29295⁠

name =

⁠theta195⁠

short.name =

⁠theta195⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta196'
hyperid =

⁠29296⁠

name =

⁠theta196⁠

short.name =

⁠theta196⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta197'
hyperid =

⁠29297⁠

name =

⁠theta197⁠

short.name =

⁠theta197⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta198'
hyperid =

⁠29298⁠

name =

⁠theta198⁠

short.name =

⁠theta198⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta199'
hyperid =

⁠29299⁠

name =

⁠theta199⁠

short.name =

⁠theta199⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta200'
hyperid =

⁠29300⁠

name =

⁠theta200⁠

short.name =

⁠theta200⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta201'
hyperid =

⁠29301⁠

name =

⁠theta201⁠

short.name =

⁠theta201⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta202'
hyperid =

⁠29302⁠

name =

⁠theta202⁠

short.name =

⁠theta202⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta203'
hyperid =

⁠29303⁠

name =

⁠theta203⁠

short.name =

⁠theta203⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta204'
hyperid =

⁠29304⁠

name =

⁠theta204⁠

short.name =

⁠theta204⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta205'
hyperid =

⁠29305⁠

name =

⁠theta205⁠

short.name =

⁠theta205⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta206'
hyperid =

⁠29306⁠

name =

⁠theta206⁠

short.name =

⁠theta206⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta207'
hyperid =

⁠29307⁠

name =

⁠theta207⁠

short.name =

⁠theta207⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta208'
hyperid =

⁠29308⁠

name =

⁠theta208⁠

short.name =

⁠theta208⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta209'
hyperid =

⁠29309⁠

name =

⁠theta209⁠

short.name =

⁠theta209⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta210'
hyperid =

⁠29310⁠

name =

⁠theta210⁠

short.name =

⁠theta210⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta211'
hyperid =

⁠29311⁠

name =

⁠theta211⁠

short.name =

⁠theta211⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta212'
hyperid =

⁠29312⁠

name =

⁠theta212⁠

short.name =

⁠theta212⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta213'
hyperid =

⁠29313⁠

name =

⁠theta213⁠

short.name =

⁠theta213⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta214'
hyperid =

⁠29314⁠

name =

⁠theta214⁠

short.name =

⁠theta214⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta215'
hyperid =

⁠29315⁠

name =

⁠theta215⁠

short.name =

⁠theta215⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta216'
hyperid =

⁠29316⁠

name =

⁠theta216⁠

short.name =

⁠theta216⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta217'
hyperid =

⁠29317⁠

name =

⁠theta217⁠

short.name =

⁠theta217⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta218'
hyperid =

⁠29318⁠

name =

⁠theta218⁠

short.name =

⁠theta218⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta219'
hyperid =

⁠29319⁠

name =

⁠theta219⁠

short.name =

⁠theta219⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta220'
hyperid =

⁠29320⁠

name =

⁠theta220⁠

short.name =

⁠theta220⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta221'
hyperid =

⁠29321⁠

name =

⁠theta221⁠

short.name =

⁠theta221⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta222'
hyperid =

⁠29322⁠

name =

⁠theta222⁠

short.name =

⁠theta222⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta223'
hyperid =

⁠29323⁠

name =

⁠theta223⁠

short.name =

⁠theta223⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta224'
hyperid =

⁠29324⁠

name =

⁠theta224⁠

short.name =

⁠theta224⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta225'
hyperid =

⁠29325⁠

name =

⁠theta225⁠

short.name =

⁠theta225⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta226'
hyperid =

⁠29326⁠

name =

⁠theta226⁠

short.name =

⁠theta226⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta227'
hyperid =

⁠29327⁠

name =

⁠theta227⁠

short.name =

⁠theta227⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta228'
hyperid =

⁠29328⁠

name =

⁠theta228⁠

short.name =

⁠theta228⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta229'
hyperid =

⁠29329⁠

name =

⁠theta229⁠

short.name =

⁠theta229⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta230'
hyperid =

⁠29330⁠

name =

⁠theta230⁠

short.name =

⁠theta230⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta231'
hyperid =

⁠29331⁠

name =

⁠theta231⁠

short.name =

⁠theta231⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta232'
hyperid =

⁠29332⁠

name =

⁠theta232⁠

short.name =

⁠theta232⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta233'
hyperid =

⁠29333⁠

name =

⁠theta233⁠

short.name =

⁠theta233⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta234'
hyperid =

⁠29334⁠

name =

⁠theta234⁠

short.name =

⁠theta234⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta235'
hyperid =

⁠29335⁠

name =

⁠theta235⁠

short.name =

⁠theta235⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta236'
hyperid =

⁠29336⁠

name =

⁠theta236⁠

short.name =

⁠theta236⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta237'
hyperid =

⁠29337⁠

name =

⁠theta237⁠

short.name =

⁠theta237⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta238'
hyperid =

⁠29338⁠

name =

⁠theta238⁠

short.name =

⁠theta238⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta239'
hyperid =

⁠29339⁠

name =

⁠theta239⁠

short.name =

⁠theta239⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta240'
hyperid =

⁠29340⁠

name =

⁠theta240⁠

short.name =

⁠theta240⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta241'
hyperid =

⁠29341⁠

name =

⁠theta241⁠

short.name =

⁠theta241⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta242'
hyperid =

⁠29342⁠

name =

⁠theta242⁠

short.name =

⁠theta242⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta243'
hyperid =

⁠29343⁠

name =

⁠theta243⁠

short.name =

⁠theta243⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta244'
hyperid =

⁠29344⁠

name =

⁠theta244⁠

short.name =

⁠theta244⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta245'
hyperid =

⁠29345⁠

name =

⁠theta245⁠

short.name =

⁠theta245⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta246'
hyperid =

⁠29346⁠

name =

⁠theta246⁠

short.name =

⁠theta246⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta247'
hyperid =

⁠29347⁠

name =

⁠theta247⁠

short.name =

⁠theta247⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta248'
hyperid =

⁠29348⁠

name =

⁠theta248⁠

short.name =

⁠theta248⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta249'
hyperid =

⁠29349⁠

name =

⁠theta249⁠

short.name =

⁠theta249⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta250'
hyperid =

⁠29350⁠

name =

⁠theta250⁠

short.name =

⁠theta250⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta251'
hyperid =

⁠29351⁠

name =

⁠theta251⁠

short.name =

⁠theta251⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta252'
hyperid =

⁠29352⁠

name =

⁠theta252⁠

short.name =

⁠theta252⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta253'
hyperid =

⁠29353⁠

name =

⁠theta253⁠

short.name =

⁠theta253⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta254'
hyperid =

⁠29354⁠

name =

⁠theta254⁠

short.name =

⁠theta254⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta255'
hyperid =

⁠29355⁠

name =

⁠theta255⁠

short.name =

⁠theta255⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta256'
hyperid =

⁠29356⁠

name =

⁠theta256⁠

short.name =

⁠theta256⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta257'
hyperid =

⁠29357⁠

name =

⁠theta257⁠

short.name =

⁠theta257⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta258'
hyperid =

⁠29358⁠

name =

⁠theta258⁠

short.name =

⁠theta258⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta259'
hyperid =

⁠29359⁠

name =

⁠theta259⁠

short.name =

⁠theta259⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta260'
hyperid =

⁠29360⁠

name =

⁠theta260⁠

short.name =

⁠theta260⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta261'
hyperid =

⁠29361⁠

name =

⁠theta261⁠

short.name =

⁠theta261⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta262'
hyperid =

⁠29362⁠

name =

⁠theta262⁠

short.name =

⁠theta262⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta263'
hyperid =

⁠29363⁠

name =

⁠theta263⁠

short.name =

⁠theta263⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta264'
hyperid =

⁠29364⁠

name =

⁠theta264⁠

short.name =

⁠theta264⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta265'
hyperid =

⁠29365⁠

name =

⁠theta265⁠

short.name =

⁠theta265⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta266'
hyperid =

⁠29366⁠

name =

⁠theta266⁠

short.name =

⁠theta266⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta267'
hyperid =

⁠29367⁠

name =

⁠theta267⁠

short.name =

⁠theta267⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta268'
hyperid =

⁠29368⁠

name =

⁠theta268⁠

short.name =

⁠theta268⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta269'
hyperid =

⁠29369⁠

name =

⁠theta269⁠

short.name =

⁠theta269⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta270'
hyperid =

⁠29370⁠

name =

⁠theta270⁠

short.name =

⁠theta270⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta271'
hyperid =

⁠29371⁠

name =

⁠theta271⁠

short.name =

⁠theta271⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta272'
hyperid =

⁠29372⁠

name =

⁠theta272⁠

short.name =

⁠theta272⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta273'
hyperid =

⁠29373⁠

name =

⁠theta273⁠

short.name =

⁠theta273⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta274'
hyperid =

⁠29374⁠

name =

⁠theta274⁠

short.name =

⁠theta274⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta275'
hyperid =

⁠29375⁠

name =

⁠theta275⁠

short.name =

⁠theta275⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta276'
hyperid =

⁠29376⁠

name =

⁠theta276⁠

short.name =

⁠theta276⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta277'
hyperid =

⁠29377⁠

name =

⁠theta277⁠

short.name =

⁠theta277⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta278'
hyperid =

⁠29378⁠

name =

⁠theta278⁠

short.name =

⁠theta278⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta279'
hyperid =

⁠29379⁠

name =

⁠theta279⁠

short.name =

⁠theta279⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta280'
hyperid =

⁠29380⁠

name =

⁠theta280⁠

short.name =

⁠theta280⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta281'
hyperid =

⁠29381⁠

name =

⁠theta281⁠

short.name =

⁠theta281⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta282'
hyperid =

⁠29382⁠

name =

⁠theta282⁠

short.name =

⁠theta282⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta283'
hyperid =

⁠29383⁠

name =

⁠theta283⁠

short.name =

⁠theta283⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta284'
hyperid =

⁠29384⁠

name =

⁠theta284⁠

short.name =

⁠theta284⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta285'
hyperid =

⁠29385⁠

name =

⁠theta285⁠

short.name =

⁠theta285⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta286'
hyperid =

⁠29386⁠

name =

⁠theta286⁠

short.name =

⁠theta286⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta287'
hyperid =

⁠29387⁠

name =

⁠theta287⁠

short.name =

⁠theta287⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta288'
hyperid =

⁠29388⁠

name =

⁠theta288⁠

short.name =

⁠theta288⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta289'
hyperid =

⁠29389⁠

name =

⁠theta289⁠

short.name =

⁠theta289⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta290'
hyperid =

⁠29390⁠

name =

⁠theta290⁠

short.name =

⁠theta290⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta291'
hyperid =

⁠29391⁠

name =

⁠theta291⁠

short.name =

⁠theta291⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta292'
hyperid =

⁠29392⁠

name =

⁠theta292⁠

short.name =

⁠theta292⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta293'
hyperid =

⁠29393⁠

name =

⁠theta293⁠

short.name =

⁠theta293⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta294'
hyperid =

⁠29394⁠

name =

⁠theta294⁠

short.name =

⁠theta294⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta295'
hyperid =

⁠29395⁠

name =

⁠theta295⁠

short.name =

⁠theta295⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta296'
hyperid =

⁠29396⁠

name =

⁠theta296⁠

short.name =

⁠theta296⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta297'
hyperid =

⁠29397⁠

name =

⁠theta297⁠

short.name =

⁠theta297⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta298'
hyperid =

⁠29398⁠

name =

⁠theta298⁠

short.name =

⁠theta298⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta299'
hyperid =

⁠29399⁠

name =

⁠theta299⁠

short.name =

⁠theta299⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta300'
hyperid =

⁠29400⁠

name =

⁠theta300⁠

short.name =

⁠theta300⁠

initial =

⁠1048576⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model '2diid'.
Properties:
doc =

⁠(This model is obsolute)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠1 2⁠

n.div.by =

⁠2⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠iid123d⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠30001⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠30002⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠30003⁠

name =

⁠correlation⁠

short.name =

⁠cor⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.15⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'z'.
Properties:
doc =

⁠The z-model in a classical mixed model formulation⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠z⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠31001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'rw2d'.
Properties:
doc =

⁠Thin-plate spline model⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠TRUE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠rw2d⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠32001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'rw2diid'.
Properties:
doc =

⁠Thin-plate spline with iid noise⁠

constr =

⁠TRUE⁠

nrow.ncol =

⁠TRUE⁠

augmented =

⁠TRUE⁠

aug.factor =

⁠2⁠

aug.constr =

⁠2⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠rw2diid⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠33001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.prec⁠

param =

⁠1 0.01⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠33002⁠

name =

⁠logit phi⁠

short.name =

⁠phi⁠

prior =

⁠pc⁠

param =

⁠0.5 0.5⁠

initial =

⁠3⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'slm'.
Properties:
doc =

⁠Spatial lag model⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠slm⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠34001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠34002⁠

name =

⁠rho⁠

short.name =

⁠rho⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) 1 / (1 + exp(-x))⁠

Model 'matern2d'.
Properties:
doc =

⁠Matern covariance function on a regular grid⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠TRUE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠matern2d⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠35001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠35002⁠

name =

⁠log range⁠

short.name =

⁠range⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'dmatern'.
Properties:
doc =

⁠Dense Matern field⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠TRUE⁠

set.default.values =

⁠TRUE⁠

pdf =

⁠dmatern⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠35101⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠3⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.prec⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠35102⁠

name =

⁠log range⁠

short.name =

⁠range⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.range⁠

param =

⁠1 0.5⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠35103⁠

name =

⁠log nu⁠

short.name =

⁠nu⁠

initial =

⁠-0.693147180559945⁠

fixed =

⁠TRUE⁠

prior =

⁠loggamma⁠

param =

⁠0.5 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'copy'.
Properties:
doc =

⁠Create a copy of a model component⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠copy⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠36001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(log(-(low - x) / (high - x))) } else if (is.finite(low) && is.infinite(high) && high > low) { return(log(x - low)) } else { stop("Condition not yet implemented") } }⁠

from.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(low + exp(x) / (1 + exp(x)) * (high - low)) } else if (is.finite(low) && is.infinite(high) && high > low) { return(low + exp(x)) } else { stop("Condition not yet implemented") } }⁠

Model 'scopy'.
Properties:
doc =

⁠Create a scopy of a model component⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠scopy⁠

Number of hyperparmeters is 15.

Hyperparameter 'theta1'
hyperid =

⁠36101⁠

name =

⁠mean⁠

short.name =

⁠mean⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠36102⁠

name =

⁠slope⁠

short.name =

⁠slope⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠36103⁠

name =

⁠spline.theta1⁠

short.name =

⁠spline⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠laplace⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠36104⁠

name =

⁠spline.theta2⁠

short.name =

⁠spline2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠36105⁠

name =

⁠spline.theta3⁠

short.name =

⁠spline3⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠36106⁠

name =

⁠spline.theta4⁠

short.name =

⁠spline4⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠36107⁠

name =

⁠spline.theta5⁠

short.name =

⁠spline5⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠36108⁠

name =

⁠spline.theta6⁠

short.name =

⁠spline6⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠36109⁠

name =

⁠spline.theta7⁠

short.name =

⁠spline7⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠36110⁠

name =

⁠spline.theta8⁠

short.name =

⁠spline8⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠36111⁠

name =

⁠spline.theta9⁠

short.name =

⁠spline9⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠36112⁠

name =

⁠spline.theta10⁠

short.name =

⁠spline10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠36113⁠

name =

⁠spline.theta11⁠

short.name =

⁠spline11⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠36114⁠

name =

⁠spline.theta12⁠

short.name =

⁠spline12⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠36115⁠

name =

⁠spline.theta13⁠

short.name =

⁠spline13⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'clinear'.
Properties:
doc =

⁠Constrained linear effect⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠clinear⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠37001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { stopifnot(low < high) return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(log(-(low - x) / (high - x))) } else if (is.finite(low) && is.infinite(high) && high > low) { return(log(x - low)) } else { stop("Condition not yet implemented") } }⁠

from.theta =

⁠function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { stopifnot(low < high) return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(low + exp(x) / (1 + exp(x)) * (high - low)) } else if (is.finite(low) && is.infinite(high) && high > low) { return(low + exp(x)) } else { stop("Condition not yet implemented") } }⁠

Model 'sigm'.
Properties:
doc =

⁠Sigmoidal effect of a covariate⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠sigm⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠38001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠38002⁠

name =

⁠loghalflife⁠

short.name =

⁠halflife⁠

initial =

⁠3⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠3 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠38003⁠

name =

⁠logshape⁠

short.name =

⁠shape⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠10 10⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'revsigm'.
Properties:
doc =

⁠Reverse sigmoidal effect of a covariate⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠sigm⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠39001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠39002⁠

name =

⁠loghalflife⁠

short.name =

⁠halflife⁠

initial =

⁠3⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠3 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠39003⁠

name =

⁠logshape⁠

short.name =

⁠shape⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠10 10⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'log1exp'.
Properties:
doc =

⁠A nonlinear model of a covariate⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠log1exp⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠39011⁠

name =

⁠beta⁠

short.name =

⁠b⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠39012⁠

name =

⁠alpha⁠

short.name =

⁠a⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠39013⁠

name =

⁠gamma⁠

short.name =

⁠g⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'logdist'.
Properties:
doc =

⁠A nonlinear model of a covariate⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠logdist⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠39021⁠

name =

⁠beta⁠

short.name =

⁠b⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠39022⁠

name =

⁠alpha1⁠

short.name =

⁠a1⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠0.1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠39023⁠

name =

⁠alpha2⁠

short.name =

⁠a2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠0.1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'group'

Valid models in this section are:

Model 'exchangeable'.
Properties:
doc =

⁠Exchangeable correlations⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠40001⁠

name =

⁠logit correlation⁠

short.name =

⁠rho⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.2⁠

to.theta =

⁠function(x, REPLACE.ME.ngroup) log((1 + x * (ngroup - 1)) / (1 - x))⁠

from.theta =

⁠function(x, REPLACE.ME.ngroup) (exp(x) - 1) / (exp(x) + ngroup - 1)⁠

Model 'exchangeablepos'.
Properties:
doc =

⁠Exchangeable positive correlations⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠40101⁠

name =

⁠logit correlation⁠

short.name =

⁠rho⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.5⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'ar1'.
Properties:
doc =

⁠AR(1) correlations⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠41001⁠

name =

⁠logit correlation⁠

short.name =

⁠rho⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.15⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'ar'.
Properties:
doc =

⁠AR(p) correlations⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠42001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.prec⁠

param =

⁠3 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠42002⁠

name =

⁠pacf1⁠

short.name =

⁠pacf1⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.5⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta3'
hyperid =

⁠42003⁠

name =

⁠pacf2⁠

short.name =

⁠pacf2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.4⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta4'
hyperid =

⁠42004⁠

name =

⁠pacf3⁠

short.name =

⁠pacf3⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.3⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta5'
hyperid =

⁠42005⁠

name =

⁠pacf4⁠

short.name =

⁠pacf4⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.2⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta6'
hyperid =

⁠42006⁠

name =

⁠pacf5⁠

short.name =

⁠pacf5⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta7'
hyperid =

⁠42007⁠

name =

⁠pacf6⁠

short.name =

⁠pacf6⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta8'
hyperid =

⁠42008⁠

name =

⁠pacf7⁠

short.name =

⁠pacf7⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta9'
hyperid =

⁠42009⁠

name =

⁠pacf8⁠

short.name =

⁠pacf8⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta10'
hyperid =

⁠42010⁠

name =

⁠pacf9⁠

short.name =

⁠pacf9⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Hyperparameter 'theta11'
hyperid =

⁠42011⁠

name =

⁠pacf10⁠

short.name =

⁠pacf10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.cor0⁠

param =

⁠0.5 0.1⁠

to.theta =

⁠function(x) log((1 + x) / (1 - x))⁠

from.theta =

⁠function(x) 2 * exp(x) / (1 + exp(x)) - 1⁠

Model 'rw1'.
Properties:
doc =

⁠Random walk of order 1⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠43001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'rw2'.
Properties:
doc =

⁠Random walk of order 2⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠44001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'besag'.
Properties:
doc =

⁠Besag model⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠45001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'iid'.
Properties:
doc =

⁠Independent model⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠46001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'scopy'

Valid models in this section are:

Model 'rw1'.
Properties:
doc =

⁠Random walk of order 1⁠

Number of hyperparmeters is 0.

Model 'rw2'.
Properties:
doc =

⁠Random walk of order 2⁠

Number of hyperparmeters is 0.

'mix'

Valid models in this section are:

Model 'gaussian'.
Properties:
doc =

⁠Gaussian mixture⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠47001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the Gaussian observations⁠

output.name.intern =

⁠Log precision for the Gaussian observations⁠

prior =

⁠pc.prec⁠

param =

⁠1 0.01⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'loggamma'.
Properties:
doc =

⁠LogGamma mixture⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠47101⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.mgamma⁠

param =

⁠4.8⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'mloggamma'.
Properties:
doc =

⁠Minus-LogGamma mixture⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠47201⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

prior =

⁠pc.mgamma⁠

param =

⁠4.8⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'link'

Valid models in this section are:

Model 'default'.
Properties:
doc =

⁠The default link⁠

Number of hyperparmeters is 0.

Model 'cloglog'.
Properties:
doc =

⁠The complementary log-log link⁠

Number of hyperparmeters is 0.

Model 'ccloglog'.
Properties:
doc =

⁠The complement complementary log-log link⁠

Number of hyperparmeters is 0.

Model 'loglog'.
Properties:
doc =

⁠The log-log link⁠

Number of hyperparmeters is 0.

Model 'identity'.
Properties:
doc =

⁠The identity link⁠

Number of hyperparmeters is 0.

Model 'inverse'.
Properties:
doc =

⁠The inverse link⁠

Number of hyperparmeters is 0.

Model 'log'.
Properties:
doc =

⁠The log-link⁠

Number of hyperparmeters is 0.

Model 'loga'.
Properties:
doc =

⁠The loga-link⁠

Number of hyperparmeters is 0.

Model 'neglog'.
Properties:
doc =

⁠The negative log-link⁠

Number of hyperparmeters is 0.

Model 'logit'.
Properties:
doc =

⁠The logit-link⁠

Number of hyperparmeters is 0.

Model 'probit'.
Properties:
doc =

⁠The probit-link⁠

Number of hyperparmeters is 0.

Model 'cauchit'.
Properties:
doc =

⁠The cauchit-link⁠

Number of hyperparmeters is 0.

Model 'tan'.
Properties:
doc =

⁠The tan-link⁠

pdf =

⁠circular⁠

Number of hyperparmeters is 0.

Model 'tanpi'.
Properties:
doc =

⁠The tanpi-link⁠

pdf =

⁠circular⁠

Number of hyperparmeters is 0.

Model 'quantile'.
Properties:
doc =

⁠The quantile-link⁠

Number of hyperparmeters is 0.

Model 'pquantile'.
Properties:
doc =

⁠The population quantile-link⁠

Number of hyperparmeters is 0.

Model 'sslogit'.
Properties:
doc =

⁠Logit link with sensitivity and specificity⁠

status =

⁠disabled⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠48001⁠

name =

⁠sensitivity⁠

short.name =

⁠sens⁠

prior =

⁠logitbeta⁠

param =

⁠10 5⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠48002⁠

name =

⁠specificity⁠

short.name =

⁠spec⁠

prior =

⁠logitbeta⁠

param =

⁠10 5⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'logoffset'.
Properties:
doc =

⁠Log-link with an offset⁠

pdf =

⁠logoffset⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠49001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'logitoffset'.
Properties:
doc =

⁠Logit-link with an offset⁠

pdf =

⁠logitoffset⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠49011⁠

name =

⁠prob⁠

short.name =

⁠p⁠

prior =

⁠normal⁠

param =

⁠-1 100⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'robit'.
Properties:
doc =

⁠Robit link⁠

pdf =

⁠robit⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠49021⁠

name =

⁠log degrees of freedom⁠

short.name =

⁠dof⁠

initial =

⁠1.6094379124341⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.dof⁠

param =

⁠50 0.5⁠

to.theta =

⁠function(x) log(x - 2)⁠

from.theta =

⁠function(x) 2 + exp(x)⁠

Model 'sn'.
Properties:
doc =

⁠Skew-normal link⁠

pdf =

⁠linksn⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠49031⁠

name =

⁠skewness⁠

short.name =

⁠skew⁠

initial =

⁠0.00123456789⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.sn⁠

param =

⁠10⁠

to.theta =

⁠function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))⁠

from.theta =

⁠function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)⁠

Hyperparameter 'theta2'
hyperid =

⁠49032⁠

name =

⁠intercept⁠

short.name =

⁠p0⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠linksnintercept⁠

param =

⁠0 0⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'gevit'.
Properties:
doc =

⁠GEVIT link⁠

pdf =

⁠gevit⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠49033⁠

name =

⁠gev tail⁠

short.name =

⁠tail⁠

initial =

⁠0.1⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.egptail⁠

param =

⁠5 -0.5 0.5⁠

to.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠49034⁠

name =

⁠gev p0⁠

short.name =

⁠p0⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) 1 / (1 + exp(-x))⁠

Model 'cgevit'.
Properties:
doc =

⁠Complement GEVIT link⁠

pdf =

⁠gevit⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠49035⁠

name =

⁠gev tail⁠

short.name =

⁠tail⁠

initial =

⁠-3⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.gevtail⁠

param =

⁠7 0 0.5⁠

to.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠49036⁠

name =

⁠gev p0⁠

short.name =

⁠p0⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) 1 / (1 + exp(-x))⁠

Model 'powerlogit'.
Properties:
doc =

⁠Power logit link⁠

pdf =

⁠powerlogit⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠49131⁠

name =

⁠power⁠

short.name =

⁠power⁠

initial =

⁠0.00123456789⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠49132⁠

name =

⁠intercept⁠

short.name =

⁠p0⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠logitbeta⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'test1'.
Properties:
doc =

⁠A test1-link function (experimental)⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠50001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'special1'.
Properties:
doc =

⁠A special1-link function (experimental)⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠51001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠51002⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠mvnorm⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠51003⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠51004⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠51005⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠51006⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠51007⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠51008⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠51009⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠51010⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠51011⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'special2'.
Properties:
doc =

⁠A special2-link function (experimental)⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠52001⁠

name =

⁠beta⁠

short.name =

⁠b⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

'predictor'

Valid models in this section are:

Model 'predictor'.
Properties:
doc =

⁠(do not use)⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠53001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠13.8155105579643⁠

fixed =

⁠TRUE⁠

prior =

⁠loggamma⁠

param =

⁠1 1e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'hazard'

Valid models in this section are:

Model 'rw1'.
Properties:
doc =

⁠A random walk of order 1 for the log-hazard⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠54001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'rw2'.
Properties:
doc =

⁠A random walk of order 2 for the log-hazard⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠55001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'iid'.
Properties:
doc =

⁠An iid model for the log-hazard⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠55501⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'likelihood'

Valid models in this section are:

Model 'fl'.
Properties:
doc =

⁠The fl likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default identity⁠

status =

⁠experimental⁠

pdf =

⁠fl⁠

Number of hyperparmeters is 0.

Model 'poisson'.
Properties:
doc =

⁠The Poisson likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset quantile test1 special1 special2⁠

pdf =

⁠poisson⁠

Number of hyperparmeters is 0.

Model 'npoisson'.
Properties:
doc =

⁠The Normal approximation to the Poisson likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset⁠

pdf =

⁠poisson⁠

Number of hyperparmeters is 0.

Model 'nzpoisson'.
Properties:
doc =

⁠The nzPoisson likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset⁠

pdf =

⁠nzpoisson⁠

Number of hyperparmeters is 0.

Model 'xpoisson'.
Properties:
doc =

⁠The Poisson likelihood (expert version)⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset quantile test1 special1 special2⁠

pdf =

⁠poisson⁠

Number of hyperparmeters is 0.

Model 'cenpoisson'.
Properties:
doc =

⁠Then censored Poisson likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset test1 special1 special2⁠

pdf =

⁠cenpoisson⁠

Number of hyperparmeters is 0.

Model 'cenpoisson2'.
Properties:
doc =

⁠Then censored Poisson likelihood (version 2)⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset test1 special1 special2⁠

pdf =

⁠cenpoisson2⁠

Number of hyperparmeters is 0.

Model 'gpoisson'.
Properties:
doc =

⁠The generalized Poisson likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset⁠

pdf =

⁠gpoisson⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠56001⁠

name =

⁠overdispersion⁠

short.name =

⁠phi⁠

output.name =

⁠Overdispersion for gpoisson⁠

output.name.intern =

⁠Log overdispersion for gpoisson⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠56002⁠

name =

⁠p⁠

short.name =

⁠p⁠

output.name =

⁠Parameter p for gpoisson⁠

output.name.intern =

⁠Parameter p_intern for gpoisson⁠

initial =

⁠1⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠1 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'poisson.special1'.
Properties:
doc =

⁠The Poisson.special1 likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log⁠

pdf =

⁠poisson-special⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠56100⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠one-probability parameter for poisson.special1⁠

output.name.intern =

⁠intern one-probability parameter for poisson.special1⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model '0poisson'.
Properties:
doc =

⁠New 0-inflated Poisson⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log quantile⁠

link.simple =

⁠default logit cauchit probit cloglog ccloglog⁠

pdf =

⁠0inflated⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠56201⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for 0poisson observations⁠

output.name.intern =

⁠beta1 for 0poisson observations⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠56202⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for 0poisson observations⁠

output.name.intern =

⁠beta2 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠56203⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for 0poisson observations⁠

output.name.intern =

⁠beta3 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠56204⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for 0poisson observations⁠

output.name.intern =

⁠beta4 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠56205⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for 0poisson observations⁠

output.name.intern =

⁠beta5 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠56206⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for 0poisson observations⁠

output.name.intern =

⁠beta6 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠56207⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for 0poisson observations⁠

output.name.intern =

⁠beta7 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠56208⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for 0poisson observations⁠

output.name.intern =

⁠beta8 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠56209⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for 0poisson observations⁠

output.name.intern =

⁠beta9 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠56210⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for 0poisson observations⁠

output.name.intern =

⁠beta10 for 0poisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model '0poissonS'.
Properties:
doc =

⁠New 0-inflated Poisson Swap⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit⁠

link.simple =

⁠default log⁠

pdf =

⁠0inflated⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠56301⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for 0poissonS observations⁠

output.name.intern =

⁠beta1 for 0poissonS observations⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠56302⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for 0poissonS observations⁠

output.name.intern =

⁠beta2 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠56303⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for 0poissonS observations⁠

output.name.intern =

⁠beta3 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠56304⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for 0poissonS observations⁠

output.name.intern =

⁠beta4 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠56305⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for 0poissonS observations⁠

output.name.intern =

⁠beta5 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠56306⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for 0poissonS observations⁠

output.name.intern =

⁠beta6 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠56307⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for 0poissonS observations⁠

output.name.intern =

⁠beta7 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠56308⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for 0poissonS observations⁠

output.name.intern =

⁠beta8 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠56309⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for 0poissonS observations⁠

output.name.intern =

⁠beta9 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠56310⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for 0poissonS observations⁠

output.name.intern =

⁠beta10 for 0poissonS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'bell'.
Properties:
doc =

⁠The Bell likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log⁠

pdf =

⁠bell⁠

Number of hyperparmeters is 0.

Model '0binomial'.
Properties:
doc =

⁠New 0-inflated Binomial⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog log⁠

link.simple =

⁠default logit cauchit probit cloglog ccloglog⁠

pdf =

⁠0inflated⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠56401⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for 0binomial observations⁠

output.name.intern =

⁠beta1 for 0binomial observations⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠56402⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for 0binomial observations⁠

output.name.intern =

⁠beta2 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠56403⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for 0binomial observations⁠

output.name.intern =

⁠beta3 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠56404⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for 0binomial observations⁠

output.name.intern =

⁠beta4 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠56405⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for 0binomial observations⁠

output.name.intern =

⁠beta5 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠56406⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for 0binomial observations⁠

output.name.intern =

⁠beta6 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠56407⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for 0binomial observations⁠

output.name.intern =

⁠beta7 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠56408⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for 0binomial observations⁠

output.name.intern =

⁠beta8 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠56409⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for 0binomial observations⁠

output.name.intern =

⁠beta9 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠56410⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for 0binomial observations⁠

output.name.intern =

⁠beta10 for 0binomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model '0binomialS'.
Properties:
doc =

⁠New 0-inflated Binomial Swap⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog log⁠

link.simple =

⁠default logit cauchit probit cloglog ccloglog⁠

pdf =

⁠0inflated⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠56501⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for 0binomialS observations⁠

output.name.intern =

⁠beta1 for 0binomialS observations⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠56502⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for 0binomialS observations⁠

output.name.intern =

⁠beta2 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠56503⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for 0binomialS observations⁠

output.name.intern =

⁠beta3 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠56504⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for 0binomialS observations⁠

output.name.intern =

⁠beta4 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠56505⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for 0binomialS observations⁠

output.name.intern =

⁠beta5 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠56506⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for 0binomialS observations⁠

output.name.intern =

⁠beta6 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠56507⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for 0binomialS observations⁠

output.name.intern =

⁠beta7 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠56508⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for 0binomialS observations⁠

output.name.intern =

⁠beta8 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠56509⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for 0binomialS observations⁠

output.name.intern =

⁠beta9 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠56510⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for 0binomialS observations⁠

output.name.intern =

⁠beta10 for 0binomialS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'binomialmix'.
Properties:
doc =

⁠Binomial mixture⁠

status =

⁠experimental⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit probit⁠

pdf =

⁠binomialmix⁠

Number of hyperparmeters is 51.

Hyperparameter 'theta1'
hyperid =

⁠56551⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for binomialmix observations⁠

output.name.intern =

⁠beta1 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠56552⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for binomialmix observations⁠

output.name.intern =

⁠beta2 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠56553⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for binomialmix observations⁠

output.name.intern =

⁠beta3 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠56554⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for binomialmix observations⁠

output.name.intern =

⁠beta4 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠56555⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for binomialmix observations⁠

output.name.intern =

⁠beta5 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠56556⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for binomialmix observations⁠

output.name.intern =

⁠beta6 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠56557⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for binomialmix observations⁠

output.name.intern =

⁠beta7 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠56558⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for binomialmix observations⁠

output.name.intern =

⁠beta8 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠56559⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for binomialmix observations⁠

output.name.intern =

⁠beta9 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠56560⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for binomialmix observations⁠

output.name.intern =

⁠beta10 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠56561⁠

name =

⁠beta11⁠

short.name =

⁠beta11⁠

output.name =

⁠beta11 for binomialmix observations⁠

output.name.intern =

⁠beta11 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠56562⁠

name =

⁠beta12⁠

short.name =

⁠beta12⁠

output.name =

⁠beta12 for binomialmix observations⁠

output.name.intern =

⁠beta12 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠56563⁠

name =

⁠beta13⁠

short.name =

⁠beta13⁠

output.name =

⁠beta13 for binomialmix observations⁠

output.name.intern =

⁠beta13 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠56564⁠

name =

⁠beta14⁠

short.name =

⁠beta14⁠

output.name =

⁠beta14 for binomialmix observations⁠

output.name.intern =

⁠beta14 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠56565⁠

name =

⁠beta15⁠

short.name =

⁠beta15⁠

output.name =

⁠beta15 for binomialmix observations⁠

output.name.intern =

⁠beta15 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠56566⁠

name =

⁠beta16⁠

short.name =

⁠beta16⁠

output.name =

⁠beta16 for binomialmix observations⁠

output.name.intern =

⁠beta16 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta17'
hyperid =

⁠56567⁠

name =

⁠beta17⁠

short.name =

⁠beta17⁠

output.name =

⁠beta17 for binomialmix observations⁠

output.name.intern =

⁠beta17 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta18'
hyperid =

⁠56568⁠

name =

⁠beta18⁠

short.name =

⁠beta18⁠

output.name =

⁠beta18 for binomialmix observations⁠

output.name.intern =

⁠beta18 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta19'
hyperid =

⁠56569⁠

name =

⁠beta19⁠

short.name =

⁠beta19⁠

output.name =

⁠beta19 for binomialmix observations⁠

output.name.intern =

⁠beta19 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta20'
hyperid =

⁠56570⁠

name =

⁠beta20⁠

short.name =

⁠beta20⁠

output.name =

⁠beta20 for binomialmix observations⁠

output.name.intern =

⁠beta20 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta21'
hyperid =

⁠56571⁠

name =

⁠beta21⁠

short.name =

⁠beta21⁠

output.name =

⁠beta21 for binomialmix observations⁠

output.name.intern =

⁠beta21 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta22'
hyperid =

⁠56572⁠

name =

⁠beta22⁠

short.name =

⁠beta22⁠

output.name =

⁠beta22 for binomialmix observations⁠

output.name.intern =

⁠beta22 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta23'
hyperid =

⁠56573⁠

name =

⁠beta23⁠

short.name =

⁠beta23⁠

output.name =

⁠beta23 for binomialmix observations⁠

output.name.intern =

⁠beta23 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta24'
hyperid =

⁠56574⁠

name =

⁠beta24⁠

short.name =

⁠beta24⁠

output.name =

⁠beta24 for binomialmix observations⁠

output.name.intern =

⁠beta24 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta25'
hyperid =

⁠56575⁠

name =

⁠beta25⁠

short.name =

⁠beta25⁠

output.name =

⁠beta25 for binomialmix observations⁠

output.name.intern =

⁠beta25 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta26'
hyperid =

⁠56576⁠

name =

⁠beta26⁠

short.name =

⁠beta26⁠

output.name =

⁠beta26 for binomialmix observations⁠

output.name.intern =

⁠beta26 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta27'
hyperid =

⁠56577⁠

name =

⁠beta27⁠

short.name =

⁠beta27⁠

output.name =

⁠beta27 for binomialmix observations⁠

output.name.intern =

⁠beta27 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta28'
hyperid =

⁠56578⁠

name =

⁠beta28⁠

short.name =

⁠beta28⁠

output.name =

⁠beta28 for binomialmix observations⁠

output.name.intern =

⁠beta28 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta29'
hyperid =

⁠56579⁠

name =

⁠beta29⁠

short.name =

⁠beta29⁠

output.name =

⁠beta29 for binomialmix observations⁠

output.name.intern =

⁠beta29 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta30'
hyperid =

⁠56580⁠

name =

⁠beta30⁠

short.name =

⁠beta30⁠

output.name =

⁠beta30 for binomialmix observations⁠

output.name.intern =

⁠beta30 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta31'
hyperid =

⁠56581⁠

name =

⁠beta31⁠

short.name =

⁠beta31⁠

output.name =

⁠beta31 for binomialmix observations⁠

output.name.intern =

⁠beta31 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta32'
hyperid =

⁠56582⁠

name =

⁠beta32⁠

short.name =

⁠beta32⁠

output.name =

⁠beta32 for binomialmix observations⁠

output.name.intern =

⁠beta32 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta33'
hyperid =

⁠56583⁠

name =

⁠beta33⁠

short.name =

⁠beta33⁠

output.name =

⁠beta33 for binomialmix observations⁠

output.name.intern =

⁠beta33 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta34'
hyperid =

⁠56584⁠

name =

⁠beta34⁠

short.name =

⁠beta34⁠

output.name =

⁠beta34 for binomialmix observations⁠

output.name.intern =

⁠beta34 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta35'
hyperid =

⁠56585⁠

name =

⁠beta35⁠

short.name =

⁠beta35⁠

output.name =

⁠beta35 for binomialmix observations⁠

output.name.intern =

⁠beta35 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta36'
hyperid =

⁠56586⁠

name =

⁠beta36⁠

short.name =

⁠beta36⁠

output.name =

⁠beta36 for binomialmix observations⁠

output.name.intern =

⁠beta36 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta37'
hyperid =

⁠56587⁠

name =

⁠beta37⁠

short.name =

⁠beta37⁠

output.name =

⁠beta37 for binomialmix observations⁠

output.name.intern =

⁠beta37 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta38'
hyperid =

⁠56588⁠

name =

⁠beta38⁠

short.name =

⁠beta38⁠

output.name =

⁠beta38 for binomialmix observations⁠

output.name.intern =

⁠beta38 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta39'
hyperid =

⁠56589⁠

name =

⁠beta39⁠

short.name =

⁠beta39⁠

output.name =

⁠beta39 for binomialmix observations⁠

output.name.intern =

⁠beta39 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta40'
hyperid =

⁠56590⁠

name =

⁠beta40⁠

short.name =

⁠beta40⁠

output.name =

⁠beta40 for binomialmix observations⁠

output.name.intern =

⁠beta40 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta41'
hyperid =

⁠56591⁠

name =

⁠beta41⁠

short.name =

⁠beta41⁠

output.name =

⁠beta41 for binomialmix observations⁠

output.name.intern =

⁠beta41 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta42'
hyperid =

⁠56592⁠

name =

⁠beta42⁠

short.name =

⁠beta42⁠

output.name =

⁠beta42 for binomialmix observations⁠

output.name.intern =

⁠beta42 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta43'
hyperid =

⁠56593⁠

name =

⁠beta43⁠

short.name =

⁠beta43⁠

output.name =

⁠beta43 for binomialmix observations⁠

output.name.intern =

⁠beta43 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta44'
hyperid =

⁠56594⁠

name =

⁠beta44⁠

short.name =

⁠beta44⁠

output.name =

⁠beta44 for binomialmix observations⁠

output.name.intern =

⁠beta44 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta45'
hyperid =

⁠56595⁠

name =

⁠beta45⁠

short.name =

⁠beta45⁠

output.name =

⁠beta45 for binomialmix observations⁠

output.name.intern =

⁠beta45 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta46'
hyperid =

⁠56596⁠

name =

⁠beta46⁠

short.name =

⁠beta46⁠

output.name =

⁠beta46 for binomialmix observations⁠

output.name.intern =

⁠beta46 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta47'
hyperid =

⁠56597⁠

name =

⁠beta47⁠

short.name =

⁠beta47⁠

output.name =

⁠beta47 for binomialmix observations⁠

output.name.intern =

⁠beta47 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta48'
hyperid =

⁠56598⁠

name =

⁠beta48⁠

short.name =

⁠beta48⁠

output.name =

⁠beta48 for binomialmix observations⁠

output.name.intern =

⁠beta48 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta49'
hyperid =

⁠56599⁠

name =

⁠beta49⁠

short.name =

⁠beta49⁠

output.name =

⁠beta49 for binomialmix observations⁠

output.name.intern =

⁠beta49 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta50'
hyperid =

⁠56600⁠

name =

⁠beta50⁠

short.name =

⁠beta50⁠

output.name =

⁠beta50 for binomialmix observations⁠

output.name.intern =

⁠beta50 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta51'
hyperid =

⁠56601⁠

name =

⁠beta51⁠

short.name =

⁠beta51⁠

output.name =

⁠beta51 for binomialmix observations⁠

output.name.intern =

⁠beta51 for binomialmix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'binomial'.
Properties:
doc =

⁠The Binomial likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit gevit cgevit⁠

pdf =

⁠binomial⁠

Number of hyperparmeters is 0.

Model 'xbinomial'.
Properties:
doc =

⁠The Binomial likelihood (experimental version)⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit gevit cgevit⁠

pdf =

⁠binomial⁠

Number of hyperparmeters is 0.

Model 'occupancy'.
Properties:
doc =

⁠Occupancy likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit cloglog⁠

link.simple =

⁠default logit cloglog⁠

pdf =

⁠occupancy⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠56601⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for occupancy observations⁠

output.name.intern =

⁠beta1 for occupancy observations⁠

initial =

⁠-2⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-2 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠56602⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for occupancy observations⁠

output.name.intern =

⁠beta2 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠56603⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for occupancy observations⁠

output.name.intern =

⁠beta3 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠56604⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for occupancy observations⁠

output.name.intern =

⁠beta4 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠56605⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for occupancy observations⁠

output.name.intern =

⁠beta5 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠56606⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for occupancy observations⁠

output.name.intern =

⁠beta6 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠56607⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for occupancy observations⁠

output.name.intern =

⁠beta7 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠56608⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for occupancy observations⁠

output.name.intern =

⁠beta8 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠56609⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for occupancy observations⁠

output.name.intern =

⁠beta9 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠56610⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for occupancy observations⁠

output.name.intern =

⁠beta10 for occupancy observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'pom'.
Properties:
doc =

⁠Likelihood for the proportional odds model⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default identity⁠

pdf =

⁠pom⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠57101⁠

name =

⁠theta1⁠

short.name =

⁠theta1⁠

output.name =

⁠theta1 for POM⁠

output.name.intern =

⁠theta1 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠dirichlet⁠

param =

⁠3⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠57102⁠

name =

⁠theta2⁠

short.name =

⁠theta2⁠

output.name =

⁠theta2 for POM⁠

output.name.intern =

⁠theta2 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠57103⁠

name =

⁠theta3⁠

short.name =

⁠theta3⁠

output.name =

⁠theta3 for POM⁠

output.name.intern =

⁠theta3 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠57104⁠

name =

⁠theta4⁠

short.name =

⁠theta4⁠

output.name =

⁠theta4 for POM⁠

output.name.intern =

⁠theta4 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta5'
hyperid =

⁠57105⁠

name =

⁠theta5⁠

short.name =

⁠theta5⁠

output.name =

⁠theta5 for POM⁠

output.name.intern =

⁠theta5 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta6'
hyperid =

⁠57106⁠

name =

⁠theta6⁠

short.name =

⁠theta6⁠

output.name =

⁠theta6 for POM⁠

output.name.intern =

⁠theta6 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta7'
hyperid =

⁠57107⁠

name =

⁠theta7⁠

short.name =

⁠theta7⁠

output.name =

⁠theta7 for POM⁠

output.name.intern =

⁠theta7 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta8'
hyperid =

⁠57108⁠

name =

⁠theta8⁠

short.name =

⁠theta8⁠

output.name =

⁠theta8 for POM⁠

output.name.intern =

⁠theta8 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta9'
hyperid =

⁠57109⁠

name =

⁠theta9⁠

short.name =

⁠theta9⁠

output.name =

⁠theta9 for POM⁠

output.name.intern =

⁠theta9 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta10'
hyperid =

⁠57110⁠

name =

⁠theta10⁠

short.name =

⁠theta10⁠

output.name =

⁠theta10 for POM⁠

output.name.intern =

⁠theta10 for POM⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'bgev'.
Properties:
doc =

⁠The blended Generalized Extreme Value likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity log⁠

pdf =

⁠bgev⁠

Number of hyperparmeters is 12.

Hyperparameter 'theta1'
hyperid =

⁠57201⁠

name =

⁠spread⁠

short.name =

⁠sd⁠

output.name =

⁠spread for BGEV observations⁠

output.name.intern =

⁠log spread for BGEV observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 3⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠57202⁠

name =

⁠tail⁠

short.name =

⁠xi⁠

output.name =

⁠tail for BGEV observations⁠

output.name.intern =

⁠intern tail for BGEV observations⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.gevtail⁠

param =

⁠7 0 0.5⁠

to.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Hyperparameter 'theta3'
hyperid =

⁠57203⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠57204⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠57205⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠57206⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠57207⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠57208⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠57209⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠57210⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠57211⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠57212⁠

name =

⁠beta10⁠

short.name =

⁠beta⁠

output.name =

⁠MUST BE FIXED⁠

output.name.intern =

⁠MUST BE FIXED⁠

initial =

⁠NA⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 300⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'gamma'.
Properties:
doc =

⁠The Gamma likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log quantile⁠

pdf =

⁠gamma⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠58001⁠

name =

⁠precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠Precision-parameter for the Gamma observations⁠

output.name.intern =

⁠Intern precision-parameter for the Gamma observations⁠

initial =

⁠4.60517018598809⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'mgamma'.
Properties:
doc =

⁠The modal Gamma likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠mgamma⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠58002⁠

name =

⁠precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠Precision-parameter for the modal Gamma observations⁠

output.name.intern =

⁠Intern precision-parameter for the modal Gamma observations⁠

initial =

⁠4.60517018598809⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'gammasurv'.
Properties:
doc =

⁠The Gamma likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog quantile⁠

pdf =

⁠gammasurv⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠58101⁠

name =

⁠precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠Precision-parameter for the Gamma surv observations⁠

output.name.intern =

⁠Intern precision-parameter for the Gamma surv observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠58102⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for Gamma-Cure⁠

output.name.intern =

⁠beta1 for Gamma-Cure⁠

initial =

⁠-7⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠58103⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for Gamma-Cure⁠

output.name.intern =

⁠beta2 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠58104⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for Gamma-Cure⁠

output.name.intern =

⁠beta3 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠58105⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for Ga mma-Cure⁠

output.name.intern =

⁠beta4 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠58106⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for Gamma-Cure⁠

output.name.intern =

⁠beta5 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠58107⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for Gamma-Cure⁠

output.name.intern =

⁠beta6 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠58108⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for Gamma-Cure⁠

output.name.intern =

⁠beta7 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠58109⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for Gamma-Cure⁠

output.name.intern =

⁠beta8 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠58110⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for Gamma-Cure⁠

output.name.intern =

⁠beta9 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠58111⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for Gamma-Cure⁠

output.name.intern =

⁠beta10 for Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'mgammasurv'.
Properties:
doc =

⁠The modal Gamma likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠agamma⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠58121⁠

name =

⁠precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠Precision-parameter for the modal Gamma surv observations⁠

output.name.intern =

⁠Intern precision-parameter for the modal Gamma surv observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠58122⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for modal Gamma-Cure⁠

output.name.intern =

⁠beta1 for modal Gamma-Cure⁠

initial =

⁠-7⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠58123⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for modal Gamma-Cure⁠

output.name.intern =

⁠beta2 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠58124⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for modal Gamma-Cure⁠

output.name.intern =

⁠beta3 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠58125⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for Ga mma-Cure⁠

output.name.intern =

⁠beta4 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠58126⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for modal Gamma-Cure⁠

output.name.intern =

⁠beta5 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠58127⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for modal Gamma-Cure⁠

output.name.intern =

⁠beta6 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠58128⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for modal Gamma-Cure⁠

output.name.intern =

⁠beta7 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠58129⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for modal Gamma-Cure⁠

output.name.intern =

⁠beta8 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠58130⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for modal Gamma-Cure⁠

output.name.intern =

⁠beta9 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠58131⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for modal Gamma-Cure⁠

output.name.intern =

⁠beta10 for modal Gamma-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'gammajw'.
Properties:
doc =

⁠A special case of the Gamma likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠gammajw⁠

Number of hyperparmeters is 0.

Model 'gammajwsurv'.
Properties:
doc =

⁠A special case of the Gamma likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠gammajw⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠58200⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for GammaJW-Cure⁠

output.name.intern =

⁠beta1 for GammaJW-Cure⁠

initial =

⁠-7⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠58201⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta1 for GammaJW-Cure⁠

output.name.intern =

⁠beta1 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠58202⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for GammaJW-Cure⁠

output.name.intern =

⁠beta3 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠58203⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for GammaJW-Cure⁠

output.name.intern =

⁠beta4 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠58204⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for GammaJW-Cure⁠

output.name.intern =

⁠beta5 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠58205⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for GammaJW-Cure⁠

output.name.intern =

⁠beta6 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠58206⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for GammaJW-Cure⁠

output.name.intern =

⁠beta7 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠58207⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for GammaJW-Cure⁠

output.name.intern =

⁠beta8 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠58208⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for GammaJW-Cure⁠

output.name.intern =

⁠beta9 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠58209⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for GammaJW-Cure⁠

output.name.intern =

⁠beta10 for GammaJW-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'gammacount'.
Properties:
doc =

⁠A Gamma generalisation of the Poisson likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠gammacount⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠59001⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠Log-alpha parameter for Gammacount observations⁠

output.name.intern =

⁠Alpha parameter for Gammacount observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.gammacount⁠

param =

⁠3⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'qkumar'.
Properties:
doc =

⁠A quantile version of the Kumar likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit⁠

pdf =

⁠qkumar⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠60001⁠

name =

⁠precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠precision for qkumar observations⁠

output.name.intern =

⁠log precision for qkumar observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.1⁠

to.theta =

⁠function(x, sc = 0.1) log(x) / sc⁠

from.theta =

⁠function(x, sc = 0.1) exp(sc * x)⁠

Model 'qloglogistic'.
Properties:
doc =

⁠A quantile loglogistic likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠qloglogistic⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠60011⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠alpha for qloglogistic observations⁠

output.name.intern =

⁠log alpha for qloglogistic observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠25 25⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'qloglogisticsurv'.
Properties:
doc =

⁠A quantile loglogistic likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠qloglogistic⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠60021⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠alpha for qloglogisticsurv observations⁠

output.name.intern =

⁠log alpha for qloglogisticsurv observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠25 25⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠60022⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta1 for logLogistic-Cure⁠

initial =

⁠-5⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠60023⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta2 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠60024⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta3 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠60025⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta4 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠60026⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta5 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠60027⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta6 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠60028⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta7 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠60029⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta8 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠60030⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta9 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠60031⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for qlogLogistic-Cure⁠

output.name.intern =

⁠beta10 for qlogLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'beta'.
Properties:
doc =

⁠The Beta likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog⁠

pdf =

⁠beta⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠61001⁠

name =

⁠precision parameter⁠

short.name =

⁠phi⁠

output.name =

⁠precision parameter for the beta observations⁠

output.name.intern =

⁠intern precision-parameter for the beta observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'obeta'.
Properties:
doc =

⁠The ordered Beta likelihood⁠

status =

⁠experimental⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog⁠

pdf =

⁠obeta⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠61101⁠

name =

⁠precision parameter⁠

short.name =

⁠phi⁠

output.name =

⁠precision-parameter for the obeta observations⁠

output.name.intern =

⁠intern precision-parameter for the obeta observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠61102⁠

name =

⁠offset location⁠

short.name =

⁠loc⁠

output.name =

⁠offset location-parameter for the obeta observations⁠

output.name =

⁠offset location-parameter for the obeta observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠61103⁠

name =

⁠offset width⁠

short.name =

⁠width⁠

output.name =

⁠offset width-parameter for the obeta observations⁠

output.name =

⁠offset width-parameter for the obeta observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'betabinomial'.
Properties:
doc =

⁠The Beta-Binomial likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠betabinomial⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠62001⁠

name =

⁠overdispersion⁠

short.name =

⁠rho⁠

output.name =

⁠overdispersion for the betabinomial observations⁠

output.name.intern =

⁠intern overdispersion for the betabinomial observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 0.4⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'betabinomialna'.
Properties:
doc =

⁠The Beta-Binomial Normal approximation likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠betabinomialna⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠62101⁠

name =

⁠overdispersion⁠

short.name =

⁠rho⁠

output.name =

⁠overdispersion for the betabinomialna observations⁠

output.name.intern =

⁠intern overdispersion for the betabinomialna observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 0.4⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'cbinomial'.
Properties:
doc =

⁠The clustered Binomial likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠cbinomial⁠

Number of hyperparmeters is 0.

Model 'nbinomial'.
Properties:
doc =

⁠The negBinomial likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset quantile⁠

pdf =

⁠nbinomial⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠63001⁠

name =

⁠size⁠

short.name =

⁠size⁠

output.name =

⁠size for the nbinomial observations (1/overdispersion)⁠

output.name.intern =

⁠log size for the nbinomial observations (1/overdispersion)⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'nbinomial2'.
Properties:
doc =

⁠The negBinomial2 likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog⁠

pdf =

⁠nbinomial⁠

Number of hyperparmeters is 0.

Model 'cennbinomial2'.
Properties:
doc =

⁠The CenNegBinomial2 likelihood (similar to cenpoisson2)⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log logoffset quantile⁠

pdf =

⁠cennbinomial2⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠63101⁠

name =

⁠size⁠

short.name =

⁠size⁠

output.name =

⁠size for the cennbinomial2 observations (1/overdispersion)⁠

output.name.intern =

⁠log size for the cennbinomial2 observations (1/overdispersion)⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'simplex'.
Properties:
doc =

⁠The simplex likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog⁠

pdf =

⁠simplex⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠64001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the Simplex observations⁠

output.name.intern =

⁠Log precision for the Simplex observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'gaussian'.
Properties:
doc =

⁠The Gaussian likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity logit loga cauchit log logoffset⁠

pdf =

⁠gaussian⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠65001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the Gaussian observations⁠

output.name.intern =

⁠Log precision for the Gaussian observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠65002⁠

name =

⁠log precision offset⁠

short.name =

⁠precoffset⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠72.0873067782343⁠

fixed =

⁠TRUE⁠

prior =

⁠none⁠

param =

⁠⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'stdgaussian'.
Properties:
doc =

⁠The stdGaussian likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity logit loga cauchit log logoffset⁠

pdf =

⁠gaussian⁠

Number of hyperparmeters is 0.

Model 'gaussianjw'.
Properties:
doc =

⁠The GaussianJW likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit probit⁠

pdf =

⁠gaussianjw⁠

Number of hyperparmeters is 3.

Hyperparameter 'theta1'
hyperid =

⁠65101⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for GaussianJW observations⁠

output.name.intern =

⁠beta1 for GaussianJW observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠65102⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for GaussianJW observations⁠

output.name.intern =

⁠beta2 for GaussianJW observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠65103⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for GaussianJW observations⁠

output.name.intern =

⁠beta3 for GaussianJW observations⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-1 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'agaussian'.
Properties:
doc =

⁠The aggregated Gaussian likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity logit loga cauchit log logoffset⁠

pdf =

⁠agaussian⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠66001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the AggGaussian observations⁠

output.name.intern =

⁠Log precision for the AggGaussian observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'ggaussian'.
Properties:
doc =

⁠Generalized Gaussian⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

link.simple =

⁠default log⁠

pdf =

⁠ggaussian⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠66501⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for ggaussian observations⁠

output.name.intern =

⁠beta1 for ggaussian observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠9.33 0.61⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠66502⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for ggaussian observations⁠

output.name.intern =

⁠beta2 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠66503⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for ggaussian observations⁠

output.name.intern =

⁠beta3 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠66504⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for ggaussian observations⁠

output.name.intern =

⁠beta4 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠66505⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for ggaussian observations⁠

output.name.intern =

⁠beta5 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠66506⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for ggaussian observations⁠

output.name.intern =

⁠beta6 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠66507⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for ggaussian observations⁠

output.name.intern =

⁠beta7 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠66508⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for ggaussian observations⁠

output.name.intern =

⁠beta8 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠66509⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for ggaussian observations⁠

output.name.intern =

⁠beta9 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠66510⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for ggaussian observations⁠

output.name.intern =

⁠beta10 for ggaussian observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'ggaussianS'.
Properties:
doc =

⁠Generalized GaussianS⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

link.simple =

⁠default identity⁠

pdf =

⁠ggaussian⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠66601⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for ggaussianS observations⁠

output.name.intern =

⁠beta1 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠66602⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for ggaussianS observations⁠

output.name.intern =

⁠beta2 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠66603⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for ggaussianS observations⁠

output.name.intern =

⁠beta3 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠66604⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for ggaussianS observations⁠

output.name.intern =

⁠beta4 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠66605⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for ggaussianS observations⁠

output.name.intern =

⁠beta5 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠66606⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for ggaussianS observations⁠

output.name.intern =

⁠beta6 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠66607⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for ggaussianS observations⁠

output.name.intern =

⁠beta7 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠66608⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for ggaussianS observations⁠

output.name.intern =

⁠beta8 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠66609⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for ggaussianS observations⁠

output.name.intern =

⁠beta9 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠66610⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for ggaussianS observations⁠

output.name.intern =

⁠beta10 for ggaussianS observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.001⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'bcgaussian'.
Properties:
doc =

⁠The Box-Cox Gaussian likelihoood⁠

status =

⁠disabled⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠bcgaussian⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠65010⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the Box-Cox Gaussian observations⁠

output.name.intern =

⁠Log precision for the Box-Cox Gaussian observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠65011⁠

name =

⁠Box-Cox transformation parameter⁠

short.name =

⁠lambda⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠1 8⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'exppower'.
Properties:
doc =

⁠The exponential power likelihoood⁠

status =

⁠experimental⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity quantile⁠

pdf =

⁠exppower⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠65021⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠65022⁠

name =

⁠power⁠

short.name =

⁠beta⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) log(x-1)⁠

from.theta =

⁠function(x) 1+exp(x)⁠

Model 'sem'.
Properties:
doc =

⁠The SEM likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠sem⁠

Number of hyperparmeters is 0.

Model 'rcpoisson'.
Properties:
doc =

⁠Randomly censored Poisson⁠

status =

⁠experimental⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log⁠

pdf =

⁠rcpoisson⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠66701⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 rcpoisson observations⁠

output.name.intern =

⁠beta1 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠66702⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 rcpoisson observations⁠

output.name.intern =

⁠beta2 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠66703⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 rcpoisson observations⁠

output.name.intern =

⁠beta3 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠66704⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 rcpoisson observations⁠

output.name.intern =

⁠beta4 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠66705⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 rcpoisson observations⁠

output.name.intern =

⁠beta5 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠66706⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 rcpoisson observations⁠

output.name.intern =

⁠beta6 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠66707⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 rcpoisson observations⁠

output.name.intern =

⁠beta7 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠66708⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 rcpoisson observations⁠

output.name.intern =

⁠beta8 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠66709⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 rcpoisson observations⁠

output.name.intern =

⁠beta9 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠66710⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 rcpoisson observations⁠

output.name.intern =

⁠beta10 rcpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'tpoisson'.
Properties:
doc =

⁠Thinned Poisson⁠

status =

⁠experimental⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default log⁠

pdf =

⁠tpoisson⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠66721⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 tpoisson observations⁠

output.name.intern =

⁠beta1 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠66722⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 tpoisson observations⁠

output.name.intern =

⁠beta2 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠66723⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 tpoisson observations⁠

output.name.intern =

⁠beta3 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠66724⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 tpoisson observations⁠

output.name.intern =

⁠beta4 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠66725⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 tpoisson observations⁠

output.name.intern =

⁠beta5 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠66726⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 tpoisson observations⁠

output.name.intern =

⁠beta6 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠66727⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 tpoisson observations⁠

output.name.intern =

⁠beta7 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠66728⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 tpoisson observations⁠

output.name.intern =

⁠beta8 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠66729⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 tpoisson observations⁠

output.name.intern =

⁠beta9 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠66730⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 tpoisson observations⁠

output.name.intern =

⁠beta10 tpoisson observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'circularnormal'.
Properties:
doc =

⁠The circular Gaussian likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default tan tan.pi⁠

pdf =

⁠circular-normal⁠

status =

⁠disabled⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠67001⁠

name =

⁠log precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠Precision parameter for the Circular Normal observations⁠

output.name.intern =

⁠Log precision parameter for the Circular Normal observations⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'wrappedcauchy'.
Properties:
doc =

⁠The wrapped Cauchy likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default tan tan.pi⁠

pdf =

⁠wrapped-cauchy⁠

status =

⁠disabled⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠68001⁠

name =

⁠log precision parameter⁠

short.name =

⁠prec⁠

output.name =

⁠Precision parameter for the Wrapped Cauchy observations⁠

output.name.intern =

⁠Log precision parameter for the Wrapped Cauchy observations⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.005⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'iidgamma'.
Properties:
doc =

⁠(experimental)⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠iidgamma⁠

status =

⁠experimental⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠69001⁠

name =

⁠logshape⁠

short.name =

⁠shape⁠

output.name =

⁠Shape parameter for iid-gamma⁠

output.name.intern =

⁠Log shape parameter for iid-gamma⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠100 100⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠69002⁠

name =

⁠lograte⁠

short.name =

⁠rate⁠

output.name =

⁠Rate parameter for iid-gamma⁠

output.name.intern =

⁠Log rate parameter for iid-gamma⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠100 100⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'iidlogitbeta'.
Properties:
doc =

⁠(experimental)⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga⁠

pdf =

⁠iidlogitbeta⁠

status =

⁠experimental⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠70001⁠

name =

⁠log.a⁠

short.name =

⁠a⁠

output.name =

⁠a parameter for iid-beta⁠

output.name.intern =

⁠Log a parameter for iid-beta⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠70002⁠

name =

⁠log.b⁠

short.name =

⁠b⁠

output.name =

⁠Rate parameter for iid-gamma⁠

output.name.intern =

⁠Log rate parameter for iid-gamma⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'loggammafrailty'.
Properties:
doc =

⁠(experimental)⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠loggammafrailty⁠

status =

⁠experimental⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠71001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠precision for the gamma frailty⁠

output.name.intern =

⁠log precision for the gamma frailty⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'logistic'.
Properties:
doc =

⁠The Logistic likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠logistic⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠72001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠precision for the logistic observations⁠

output.name.intern =

⁠log precision for the logistic observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'sn'.
Properties:
doc =

⁠The Skew-Normal likelihoood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠sn⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠74001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠precision for skew-normal observations⁠

output.name.intern =

⁠log precision for skew-normal observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠74002⁠

name =

⁠logit skew⁠

short.name =

⁠skew⁠

output.name =

⁠Skewness for skew-normal observations⁠

output.name.intern =

⁠Intern skewness for skew-normal observations⁠

initial =

⁠0.00123456789⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.sn⁠

param =

⁠10⁠

to.theta =

⁠function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))⁠

from.theta =

⁠function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)⁠

Model 'gev'.
Properties:
doc =

⁠The Generalized Extreme Value likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

status =

⁠disabled: Use likelihood model 'bgev' instead; see inla.doc('bgev')⁠

pdf =

⁠gev⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠76001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠precision for GEV observations⁠

output.name.intern =

⁠log precision for GEV observations⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠76002⁠

name =

⁠tail parameter⁠

short.name =

⁠tail⁠

output.name =

⁠tail parameter for GEV observations⁠

output.name.intern =

⁠tail parameter for GEV observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 25⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'lognormal'.
Properties:
doc =

⁠The log-Normal likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠lognormal⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠77101⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the lognormal observations⁠

output.name.intern =

⁠Log precision for the lognormal observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'lognormalsurv'.
Properties:
doc =

⁠The log-Normal likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠lognormal⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠78001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for the lognormalsurv observations⁠

output.name.intern =

⁠Log precision for the lognormalsurv observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠78002⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for logNormal-Cure⁠

output.name.intern =

⁠beta1 for logNormal-Cure⁠

initial =

⁠-7⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠78003⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for logNormal-Cure⁠

output.name.intern =

⁠beta2 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠78004⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for logNormal-Cure⁠

output.name.intern =

⁠beta3 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠78005⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for logNormal-Cure⁠

output.name.intern =

⁠beta4 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠78006⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for logNormal-Cure⁠

output.name.intern =

⁠beta5 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠78007⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for logNormal-Cure⁠

output.name.intern =

⁠beta6 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠78008⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for logNormal-Cure⁠

output.name.intern =

⁠beta7 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠78009⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for logNormal-Cure⁠

output.name.intern =

⁠beta8 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠78010⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for logNormal-Cure⁠

output.name.intern =

⁠beta9 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠78011⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for logNormal-Cure⁠

output.name.intern =

⁠beta10 for logNormal-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'exponential'.
Properties:
doc =

⁠The Exponential likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠exponential⁠

Number of hyperparmeters is 0.

Model 'exponentialsurv'.
Properties:
doc =

⁠The Exponential likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠exponential⁠

Number of hyperparmeters is 10.

Hyperparameter 'theta1'
hyperid =

⁠78020⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for Exp-Cure⁠

output.name.intern =

⁠beta1 for Exp-Cure⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-1 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠78021⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for Exp-Cure⁠

output.name.intern =

⁠beta2 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠78022⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for Exp-Cure⁠

output.name.intern =

⁠beta3 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠78023⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for Exp-Cure⁠

output.name.intern =

⁠beta4 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠78024⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for Exp-Cure⁠

output.name.intern =

⁠beta5 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠78025⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for Exp-Cure⁠

output.name.intern =

⁠beta6 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠78026⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for Exp-Cure⁠

output.name.intern =

⁠beta7 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠78027⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for Exp-Cure⁠

output.name.intern =

⁠beta8 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠78028⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for Exp-Cure⁠

output.name.intern =

⁠beta9 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠78029⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for Exp-Cure⁠

output.name.intern =

⁠beta10 for Exp-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'coxph'.
Properties:
doc =

⁠Cox-proportional hazard likelihood⁠

survival =

⁠TRUE⁠

discrete =

⁠TRUE⁠

link =

⁠default log neglog⁠

pdf =

⁠coxph⁠

Number of hyperparmeters is 0.

Model 'weibull'.
Properties:
doc =

⁠The Weibull likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog quantile⁠

pdf =

⁠weibull⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠79001⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠alpha parameter for weibull⁠

output.name.intern =

⁠alpha_intern for weibull⁠

initial =

⁠-2⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.alphaw⁠

param =

⁠5⁠

to.theta =

⁠function(x, sc = 0.1) log(x) / sc⁠

from.theta =

⁠function(x, sc = 0.1) exp(sc * x)⁠

Model 'weibullsurv'.
Properties:
doc =

⁠The Weibull likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog quantile⁠

pdf =

⁠weibull⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta'
hyperid =

⁠79101⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠alpha parameter for weibullsurv⁠

output.name.intern =

⁠alpha_intern for weibullsurv⁠

initial =

⁠-2⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.alphaw⁠

param =

⁠5⁠

to.theta =

⁠function(x, sc = 0.1) log(x) / sc⁠

from.theta =

⁠function(x, sc = 0.1) exp(sc * x)⁠

Hyperparameter 'theta2'
hyperid =

⁠79102⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for Weibull-Cure⁠

output.name.intern =

⁠beta1 for Weibull-Cure⁠

initial =

⁠-7⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠79103⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for Weibull-Cure⁠

output.name.intern =

⁠beta2 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠79104⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for Weibull-Cure⁠

output.name.intern =

⁠beta3 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠79105⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for Weibull-Cure⁠

output.name.intern =

⁠beta4 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠79106⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for Weibull-Cure⁠

output.name.intern =

⁠beta5 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠79107⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for Weibull-Cure⁠

output.name.intern =

⁠beta6 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠79108⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for Weibull-Cure⁠

output.name.intern =

⁠beta7 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠79109⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for Weibull-Cure⁠

output.name.intern =

⁠beta8 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠79110⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for Weibull-Cure⁠

output.name.intern =

⁠beta9 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠79111⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for Weibull-Cure⁠

output.name.intern =

⁠beta10 for Weibull-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'loglogistic'.
Properties:
doc =

⁠The loglogistic likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠loglogistic⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠80001⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠alpha for loglogistic observations⁠

output.name.intern =

⁠log alpha for loglogistic observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠25 25⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'loglogisticsurv'.
Properties:
doc =

⁠The loglogistic likelihood (survival)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠loglogistic⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠80011⁠

name =

⁠log alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠alpha for loglogisticsurv observations⁠

output.name.intern =

⁠log alpha for loglogisticsurv observations⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠25 25⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠80012⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for logLogistic-Cure⁠

output.name.intern =

⁠beta1 for logLogistic-Cure⁠

initial =

⁠-5⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠80013⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for logLogistic-Cure⁠

output.name.intern =

⁠beta2 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠80014⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for logLogistic-Cure⁠

output.name.intern =

⁠beta3 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠80015⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for logLogistic-Cure⁠

output.name.intern =

⁠beta4 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠80016⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for logLogistic-Cure⁠

output.name.intern =

⁠beta5 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠80017⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for logLogistic-Cure⁠

output.name.intern =

⁠beta6 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠80018⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for logLogistic-Cure⁠

output.name.intern =

⁠beta7 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠80019⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for logLogistic-Cure⁠

output.name.intern =

⁠beta8 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠80020⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for logLogistic-Cure⁠

output.name.intern =

⁠beta9 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠80021⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for logLogistic-Cure⁠

output.name.intern =

⁠beta10 for logLogistic-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'stochvol'.
Properties:
doc =

⁠The Gaussian stochvol likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠stochvolgaussian⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠82001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Offset precision for stochvol⁠

output.name.intern =

⁠Log offset precision for stochvol⁠

initial =

⁠500⁠

fixed =

⁠TRUE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.005⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'stochvolln'.
Properties:
doc =

⁠The Log-Normal stochvol likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠stochvolln⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠82011⁠

name =

⁠offset⁠

short.name =

⁠c⁠

output.name =

⁠Mean offset for stochvolln⁠

output.name.intern =

⁠Mean offset for stochvolln⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'stochvolsn'.
Properties:
doc =

⁠The SkewNormal stochvol likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠stochvolsn⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠82101⁠

name =

⁠logit skew⁠

short.name =

⁠skew⁠

output.name =

⁠Skewness for stochvol_sn observations⁠

output.name.intern =

⁠Intern skewness for stochvol_sn observations⁠

initial =

⁠0.00123456789⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.sn⁠

param =

⁠10⁠

to.theta =

⁠function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))⁠

from.theta =

⁠function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)⁠

Hyperparameter 'theta2'
hyperid =

⁠82102⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠Offset precision for stochvol_sn⁠

output.name.intern =

⁠Log offset precision for stochvol_sn⁠

initial =

⁠500⁠

fixed =

⁠TRUE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.005⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'stochvolt'.
Properties:
doc =

⁠The Student-t stochvol likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠stochvolt⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠83001⁠

name =

⁠log degrees of freedom⁠

short.name =

⁠dof⁠

output.name =

⁠degrees of freedom for stochvol student-t⁠

output.name.intern =

⁠dof_intern for stochvol student-t⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.dof⁠

param =

⁠15 0.5⁠

to.theta =

⁠function(x) log(x - 2)⁠

from.theta =

⁠function(x) 2 + exp(x)⁠

Model 'stochvolnig'.
Properties:
doc =

⁠The Normal inverse Gaussian stochvol likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠stochvolnig⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠84001⁠

name =

⁠skewness⁠

short.name =

⁠skew⁠

output.name.intern =

⁠skewness_param_intern for stochvol-nig⁠

output.name =

⁠skewness parameter for stochvol-nig⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠84002⁠

name =

⁠shape⁠

short.name =

⁠shape⁠

output.name =

⁠shape parameter for stochvol-nig⁠

output.name.intern =

⁠shape_param_intern for stochvol-nig⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.5⁠

to.theta =

⁠function(x) log(x - 1)⁠

from.theta =

⁠function(x) 1 + exp(x)⁠

Model 'zeroinflatedpoisson0'.
Properties:
doc =

⁠Zero-inflated Poisson, type 0⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠85001⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated poisson_0⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated poisson_0⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedpoisson1'.
Properties:
doc =

⁠Zero-inflated Poisson, type 1⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠86001⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated poisson_1⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated poisson_1⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedpoisson2'.
Properties:
doc =

⁠Zero-inflated Poisson, type 2⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠87001⁠

name =

⁠log alpha⁠

short.name =

⁠a⁠

output.name =

⁠zero-probability parameter for zero-inflated poisson_2⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated poisson_2⁠

initial =

⁠0.693147180559945⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0.693147180559945 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'zeroinflatedcenpoisson0'.
Properties:
doc =

⁠Zero-inflated censored Poisson, type 0⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠87101⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated poisson_0⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated poisson_0⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedcenpoisson1'.
Properties:
doc =

⁠Zero-inflated censored Poisson, type 1⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠87201⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated poisson_1⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated poisson_1⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedbetabinomial0'.
Properties:
doc =

⁠Zero-inflated Beta-Binomial, type 0⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠88001⁠

name =

⁠overdispersion⁠

short.name =

⁠rho⁠

output.name =

⁠rho for zero-inflated betabinomial_0⁠

output.name.intern =

⁠rho_intern for zero-inflated betabinomial_0⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 0.4⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠88002⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated betabinomial_0⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated betabinomial_0⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedbetabinomial1'.
Properties:
doc =

⁠Zero-inflated Beta-Binomial, type 1⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠89001⁠

name =

⁠overdispersion⁠

short.name =

⁠rho⁠

output.name =

⁠rho for zero-inflated betabinomial_1⁠

output.name.intern =

⁠rho_intern for zero-inflated betabinomial_1⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 0.4⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠89002⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated betabinomial_1⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated betabinomial_1⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedbinomial0'.
Properties:
doc =

⁠Zero-inflated Binomial, type 0⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠90001⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated binomial_0⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated binomial_0⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedbinomial1'.
Properties:
doc =

⁠Zero-inflated Binomial, type 1⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠91001⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated binomial_1⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated binomial_1⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatedbinomial2'.
Properties:
doc =

⁠Zero-inflated Binomial, type 2⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠92001⁠

name =

⁠alpha⁠

short.name =

⁠alpha⁠

output.name =

⁠zero-probability parameter for zero-inflated binomial_2⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated binomial_2⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'zeroninflatedbinomial2'.
Properties:
doc =

⁠Zero and N inflated binomial, type 2⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠93001⁠

name =

⁠alpha1⁠

short.name =

⁠alpha1⁠

output.name =

⁠alpha1 parameter for zero-n-inflated binomial_2⁠

output.name.intern =

⁠intern alpha1 parameter for zero-n-inflated binomial_2⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠93002⁠

name =

⁠alpha2⁠

short.name =

⁠alpha2⁠

output.name =

⁠alpha2 parameter for zero-n-inflated binomial_2⁠

output.name.intern =

⁠intern alpha2 parameter for zero-n-inflated binomial_2⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'zeroninflatedbinomial3'.
Properties:
doc =

⁠Zero and N inflated binomial, type 3⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠93101⁠

name =

⁠alpha0⁠

short.name =

⁠alpha0⁠

output.name =

⁠alpha0 parameter for zero-n-inflated binomial_3⁠

output.name.intern =

⁠intern alpha0 parameter for zero-n-inflated binomial_3⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠93102⁠

name =

⁠alphaN⁠

short.name =

⁠alphaN⁠

output.name.intern =

⁠intern alphaN parameter for zero-n-inflated binomial_3⁠

output.name =

⁠alphaN parameter for zero-n-inflated binomial_3⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'zeroinflatedbetabinomial2'.
Properties:
doc =

⁠Zero inflated Beta-Binomial, type 2⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default logit loga cauchit probit cloglog ccloglog loglog robit sn⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠94001⁠

name =

⁠log alpha⁠

short.name =

⁠a⁠

output.name =

⁠zero-probability parameter for zero-inflated betabinomial_2⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated betabinomial_2⁠

initial =

⁠0.693147180559945⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0.693147180559945 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠94002⁠

name =

⁠beta⁠

short.name =

⁠b⁠

output.name =

⁠overdispersion parameter for zero-inflated betabinomial_2⁠

output.name.intern =

⁠intern overdispersion parameter for zero-inflated betabinomial_2⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'zeroinflatednbinomial0'.
Properties:
doc =

⁠Zero inflated negBinomial, type 0⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠95001⁠

name =

⁠log size⁠

short.name =

⁠size⁠

output.name =

⁠size for nbinomial_0 zero-inflated observations⁠

output.name.intern =

⁠log size for nbinomial_0 zero-inflated observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠95002⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated nbinomial_0⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated nbinomial_0⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatednbinomial1'.
Properties:
doc =

⁠Zero inflated negBinomial, type 1⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠96001⁠

name =

⁠log size⁠

short.name =

⁠size⁠

output.name =

⁠size for nbinomial_1 zero-inflated observations⁠

output.name.intern =

⁠log size for nbinomial_1 zero-inflated observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠96002⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability parameter for zero-inflated nbinomial_1⁠

output.name.intern =

⁠intern zero-probability parameter for zero-inflated nbinomial_1⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatednbinomial1strata2'.
Properties:
doc =

⁠Zero inflated negBinomial, type 1, strata 2⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠97001⁠

name =

⁠log size⁠

short.name =

⁠size⁠

output.name =

⁠size for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠log size for zero-inflated nbinomial_1_strata2⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠97002⁠

name =

⁠logit probability 1⁠

short.name =

⁠prob1⁠

output.name =

⁠zero-probability1 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability1 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta3'
hyperid =

⁠97003⁠

name =

⁠logit probability 2⁠

short.name =

⁠prob2⁠

output.name =

⁠zero-probability2 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability2 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta4'
hyperid =

⁠97004⁠

name =

⁠logit probability 3⁠

short.name =

⁠prob3⁠

output.name =

⁠zero-probability3 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability3 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta5'
hyperid =

⁠97005⁠

name =

⁠logit probability 4⁠

short.name =

⁠prob4⁠

output.name =

⁠zero-probability4 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability4 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta6'
hyperid =

⁠97006⁠

name =

⁠logit probability 5⁠

short.name =

⁠prob5⁠

output.name =

⁠zero-probability5 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability5 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta7'
hyperid =

⁠97007⁠

name =

⁠logit probability 6⁠

short.name =

⁠prob6⁠

output.name =

⁠zero-probability6 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability6 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta8'
hyperid =

⁠97008⁠

name =

⁠logit probability 7⁠

short.name =

⁠prob7⁠

output.name =

⁠zero-probability7 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability7 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta9'
hyperid =

⁠97009⁠

name =

⁠logit probability 8⁠

short.name =

⁠prob8⁠

output.name =

⁠zero-probability8 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability8 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta10'
hyperid =

⁠97010⁠

name =

⁠logit probability 9⁠

short.name =

⁠prob9⁠

output.name =

⁠zero-probability9 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability9 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta11'
hyperid =

⁠97011⁠

name =

⁠logit probability 10⁠

short.name =

⁠prob10⁠

output.name =

⁠zero-probability10 for zero-inflated nbinomial_1_strata2⁠

output.name.intern =

⁠intern zero-probability10 for zero-inflated nbinomial_1_strata2⁠

initial =

⁠-1⁠

fixed =

⁠TRUE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Model 'zeroinflatednbinomial1strata3'.
Properties:
doc =

⁠Zero inflated negBinomial, type 1, strata 3⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠98001⁠

name =

⁠logit probability⁠

short.name =

⁠prob⁠

output.name =

⁠zero-probability for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠intern zero-probability for zero-inflated nbinomial_1_strata3⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠-1 0.2⁠

to.theta =

⁠function(x) log(x / (1 - x))⁠

from.theta =

⁠function(x) exp(x) / (1 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠98002⁠

name =

⁠log size 1⁠

short.name =

⁠size1⁠

output.name =

⁠size1 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size1 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠98003⁠

name =

⁠log size 2⁠

short.name =

⁠size2⁠

output.name =

⁠size2 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size2 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠98004⁠

name =

⁠log size 3⁠

short.name =

⁠size3⁠

output.name =

⁠size3 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size3 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta5'
hyperid =

⁠98005⁠

name =

⁠log size 4⁠

short.name =

⁠size4⁠

output.name =

⁠size4 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size4 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta6'
hyperid =

⁠98006⁠

name =

⁠log size 5⁠

short.name =

⁠size5⁠

output.name =

⁠size5 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size5 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta7'
hyperid =

⁠98007⁠

name =

⁠log size 6⁠

short.name =

⁠size6⁠

output.name =

⁠size6 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size6 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta8'
hyperid =

⁠98008⁠

name =

⁠log size 7⁠

short.name =

⁠size7⁠

output.name =

⁠size7 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size7 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta9'
hyperid =

⁠98009⁠

name =

⁠log size 8⁠

short.name =

⁠size8⁠

output.name =

⁠size8 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size8 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta10'
hyperid =

⁠98010⁠

name =

⁠log size 9⁠

short.name =

⁠size9⁠

output.name =

⁠size9 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size9 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta11'
hyperid =

⁠98011⁠

name =

⁠log size 10⁠

short.name =

⁠size10⁠

output.name =

⁠size10 for zero-inflated nbinomial_1_strata3⁠

output.name.intern =

⁠log_size10 for zero-inflated nbinomial_1_strata3⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠TRUE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'zeroinflatednbinomial2'.
Properties:
doc =

⁠Zero inflated negBinomial, type 2⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠zeroinflated⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠99001⁠

name =

⁠log size⁠

short.name =

⁠size⁠

output.name =

⁠size for nbinomial zero-inflated observations⁠

output.name.inter =

⁠log size for nbinomial zero-inflated observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.mgamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠99002⁠

name =

⁠log alpha⁠

short.name =

⁠a⁠

output.name =

⁠parameter alpha for zero-inflated nbinomial2⁠

output.name.intern =

⁠parameter alpha.intern for zero-inflated nbinomial2⁠

initial =

⁠0.693147180559945⁠

fixed =

⁠FALSE⁠

prior =

⁠gaussian⁠

param =

⁠2 1⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 't'.
Properties:
doc =

⁠Student-t likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠student-t⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠100001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠precision for the student-t observations⁠

output.name.intern =

⁠log precision for the student-t observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠100002⁠

name =

⁠log degrees of freedom⁠

short.name =

⁠dof⁠

output.name =

⁠degrees of freedom for student-t⁠

output.name.intern =

⁠dof_intern for student-t⁠

initial =

⁠5⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.dof⁠

param =

⁠15 0.5⁠

to.theta =

⁠function(x) log(x - 2)⁠

from.theta =

⁠function(x) 2 + exp(x)⁠

Model 'tstrata'.
Properties:
doc =

⁠A stratified version of the Student-t likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠tstrata⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠101001⁠

name =

⁠log degrees of freedom⁠

short.name =

⁠dof⁠

output.name.intern =

⁠dof_intern for tstrata⁠

output.name =

⁠degrees of freedom for tstrata⁠

initial =

⁠4⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.dof⁠

param =

⁠15 0.5⁠

to.theta =

⁠function(x) log(x - 5)⁠

from.theta =

⁠function(x) 5 + exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠101002⁠

name =

⁠log precision1⁠

short.name =

⁠prec1⁠

output.name =

⁠Prec for tstrata strata⁠

output.name.intern =

⁠Log prec for tstrata strata⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta3'
hyperid =

⁠101003⁠

name =

⁠log precision2⁠

short.name =

⁠prec2⁠

output.name =

⁠Prec for tstrata strata[2]⁠

output.name.intern =

⁠Log prec for tstrata strata[2]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta4'
hyperid =

⁠101004⁠

name =

⁠log precision3⁠

short.name =

⁠prec3⁠

output.name =

⁠Prec for tstrata strata[3]⁠

output.name.intern =

⁠Log prec for tstrata strata[3]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta5'
hyperid =

⁠101005⁠

name =

⁠log precision4⁠

short.name =

⁠prec4⁠

output.name =

⁠Prec for tstrata strata[4]⁠

output.name.intern =

⁠Log prec for tstrata strata[4]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta6'
hyperid =

⁠101006⁠

name =

⁠log precision5⁠

short.name =

⁠prec5⁠

output.name =

⁠Prec for tstrata strata[5]⁠

output.name.intern =

⁠Log prec for tstrata strata[5]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta7'
hyperid =

⁠101007⁠

name =

⁠log precision6⁠

short.name =

⁠prec6⁠

output.name =

⁠Prec for tstrata strata[6]⁠

output.name.intern =

⁠Log prec for tstrata strata[6]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta8'
hyperid =

⁠101008⁠

name =

⁠log precision7⁠

short.name =

⁠prec7⁠

output.name =

⁠Prec for tstrata strata[7]⁠

output.name.intern =

⁠Log prec for tstrata strata[7]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta9'
hyperid =

⁠101009⁠

name =

⁠log precision8⁠

short.name =

⁠prec8⁠

output.name =

⁠Prec for tstrata strata[8]⁠

output.name.intern =

⁠Log prec for tstrata strata[8]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta10'
hyperid =

⁠101010⁠

name =

⁠log precision9⁠

short.name =

⁠prec9⁠

output.name =

⁠Prec for tstrata strata[9]⁠

output.name.intern =

⁠Log prec for tstrata strata[9]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta11'
hyperid =

⁠101011⁠

name =

⁠log precision10⁠

short.name =

⁠prec10⁠

output.name =

⁠Prec for tstrata strata[10]⁠

output.name.intern =

⁠Log prec for tstrata strata[10]⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'nmix'.
Properties:
doc =

⁠Binomial-Poisson mixture⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga probit⁠

pdf =

⁠nmix⁠

Number of hyperparmeters is 15.

Hyperparameter 'theta1'
hyperid =

⁠101101⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta[1] for NMix observations⁠

output.name.intern =

⁠beta[1] for NMix observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.5⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠101102⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta[2] for NMix observations⁠

output.name.intern =

⁠beta[2] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠101103⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta[3] for NMix observations⁠

output.name.intern =

⁠beta[3] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠101104⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta[4] for NMix observations⁠

output.name.intern =

⁠beta[4] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠101105⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta[5] for NMix observations⁠

output.name.intern =

⁠beta[5] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠101106⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta[6] for NMix observations⁠

output.name.intern =

⁠beta[6] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠101107⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta[7] for NMix observations⁠

output.name.intern =

⁠beta[7] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠101108⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta[8] for NMix observations⁠

output.name.intern =

⁠beta[8] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠101109⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta[9] for NMix observations⁠

output.name.intern =

⁠beta[9] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠101110⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta[10] for NMix observations⁠

output.name.intern =

⁠beta[10] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠101111⁠

name =

⁠beta11⁠

short.name =

⁠beta11⁠

output.name =

⁠beta[11] for NMix observations⁠

output.name.intern =

⁠beta[11] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠101112⁠

name =

⁠beta12⁠

short.name =

⁠beta12⁠

output.name =

⁠beta[12] for NMix observations⁠

output.name.intern =

⁠beta[12] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠101113⁠

name =

⁠beta13⁠

short.name =

⁠beta13⁠

output.name =

⁠beta[13] for NMix observations⁠

output.name.intern =

⁠beta[13] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠101114⁠

name =

⁠beta14⁠

short.name =

⁠beta14⁠

output.name =

⁠beta[14] for NMix observations⁠

output.name.intern =

⁠beta[14] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠101115⁠

name =

⁠beta15⁠

short.name =

⁠beta15⁠

output.name =

⁠beta[15] for NMix observations⁠

output.name.intern =

⁠beta[15] for NMix observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'nmixnb'.
Properties:
doc =

⁠NegBinomial-Poisson mixture⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default logit loga probit⁠

pdf =

⁠nmixnb⁠

Number of hyperparmeters is 16.

Hyperparameter 'theta1'
hyperid =

⁠101121⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta[1] for NMixNB observations⁠

output.name.intern =

⁠beta[1] for NMixNB observations⁠

initial =

⁠2.30258509299405⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 0.5⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠101122⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta[2] for NMixNB observations⁠

output.name.intern =

⁠beta[2] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠101123⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta[3] for NMixNB observations⁠

output.name.intern =

⁠beta[3] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠101124⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta[4] for NMixNB observations⁠

output.name.intern =

⁠beta[4] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠101125⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta[5] for NMixNB observations⁠

output.name.intern =

⁠beta[5] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠101126⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta[6] for NMixNB observations⁠

output.name.intern =

⁠beta[6] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠101127⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta[7] for NMixNB observations⁠

output.name.intern =

⁠beta[7] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠101128⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta[8] for NMixNB observations⁠

output.name.intern =

⁠beta[8] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠101129⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta[9] for NMixNB observations⁠

output.name.intern =

⁠beta[9] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠101130⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta[10] for NMixNB observations⁠

output.name.intern =

⁠beta[10] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠101131⁠

name =

⁠beta11⁠

short.name =

⁠beta11⁠

output.name =

⁠beta[11] for NMixNB observations⁠

output.name.intern =

⁠beta[11] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠101132⁠

name =

⁠beta12⁠

short.name =

⁠beta12⁠

output.name =

⁠beta[12] for NMixNB observations⁠

output.name.intern =

⁠beta[12] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠101133⁠

name =

⁠beta13⁠

short.name =

⁠beta13⁠

output.name =

⁠beta[13] for NMixNB observations⁠

output.name.intern =

⁠beta[13] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠101134⁠

name =

⁠beta14⁠

short.name =

⁠beta14⁠

output.name =

⁠beta[14] for NMixNB observations⁠

output.name.intern =

⁠beta[14] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠101135⁠

name =

⁠beta15⁠

short.name =

⁠beta15⁠

output.name =

⁠beta[15] for NMixNB observations⁠

output.name.intern =

⁠beta[15] for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠101136⁠

name =

⁠overdispersion⁠

short.name =

⁠overdispersion⁠

output.name =

⁠overdispersion for NMixNB observations⁠

output.name.intern =

⁠log_overdispersion for NMixNB observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.gamma⁠

param =

⁠7⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'gp'.
Properties:
doc =

⁠Generalized Pareto likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default quantile⁠

pdf =

⁠genPareto⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠101201⁠

name =

⁠tail⁠

short.name =

⁠xi⁠

output.name =

⁠Tail parameter for the gp observations⁠

output.name.intern =

⁠Intern tail parameter for the gp observations⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.gevtail⁠

param =

⁠7 0 0.5⁠

to.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Model 'egp'.
Properties:
doc =

⁠Exteneded Generalized Pareto likelihood⁠

status =

⁠experimental⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default quantile⁠

pdf =

⁠egp⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠101211⁠

name =

⁠tail⁠

short.name =

⁠xi⁠

output.name =

⁠Tail parameter for egp observations⁠

output.name.intern =

⁠Intern tail parameter for egp observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.egptail⁠

param =

⁠5 -0.5 0.5⁠

to.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠101212⁠

name =

⁠shape⁠

short.name =

⁠kappa⁠

output.name =

⁠Shape parameter for the egp observations⁠

output.name.intern =

⁠Intern shape parameter for the egp observations⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠100 100⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'dgp'.
Properties:
doc =

⁠Discrete generalized Pareto likelihood⁠

survival =

⁠FALSE⁠

discrete =

⁠TRUE⁠

link =

⁠default quantile⁠

pdf =

⁠dgp⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠101301⁠

name =

⁠tail⁠

short.name =

⁠xi⁠

output.name =

⁠Tail parameter for the dgp observations⁠

output.name.intern =

⁠Intern tail parameter for the dgp observations⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠pc.gevtail⁠

param =

⁠7 0 0.5⁠

to.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Model 'logperiodogram'.
Properties:
doc =

⁠Likelihood for the log-periodogram⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default identity⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 0.

Model 'tweedie'.
Properties:
doc =

⁠Tweedie distribution⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠tweedie⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠102101⁠

name =

⁠p⁠

short.name =

⁠p⁠

output.name =

⁠p parameter for Tweedie⁠

output.name.intern =

⁠p_intern parameter for Tweedie⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x, interval = c(1.0, 2.0)) log(-(interval[1] - x) / (interval[2] - x))⁠

from.theta =

⁠function(x, interval = c(1.0, 2.0)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))⁠

Hyperparameter 'theta2'
hyperid =

⁠102201⁠

name =

⁠dispersion⁠

short.name =

⁠phi⁠

output.name =

⁠Dispersion parameter for Tweedie⁠

output.name.intern =

⁠Log dispersion parameter for Tweedie⁠

initial =

⁠-4⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠100 100⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Model 'fmri'.
Properties:
doc =

⁠fmri distribution (special nc-chi)⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠fmri⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠103101⁠

name =

⁠precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for fmri⁠

output.name.intern =

⁠Log precision for fmri⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠10 10⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠103202⁠

name =

⁠dof⁠

short.name =

⁠df⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠4⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'fmrisurv'.
Properties:
doc =

⁠fmri distribution (special nc-chi)⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log⁠

pdf =

⁠fmri⁠

Number of hyperparmeters is 2.

Hyperparameter 'theta1'
hyperid =

⁠104101⁠

name =

⁠precision⁠

short.name =

⁠prec⁠

output.name =

⁠Precision for fmrisurv⁠

output.name.intern =

⁠Log precision for fmrisurv⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠10 10⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

Hyperparameter 'theta2'
hyperid =

⁠104201⁠

name =

⁠dof⁠

short.name =

⁠df⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠4⁠

fixed =

⁠TRUE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'gompertz'.
Properties:
doc =

⁠gompertz distribution⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠gompertz⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠105101⁠

name =

⁠shape⁠

short.name =

⁠alpha⁠

output.name.intern =

⁠alpha_intern for Gompertz⁠

output.name =

⁠alpha parameter for Gompertz⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x, sc = 0.1) log(x) / sc⁠

from.theta =

⁠function(x, sc = 0.1) exp(sc * x)⁠

Model 'gompertzsurv'.
Properties:
doc =

⁠gompertz distribution⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠gompertz⁠

Number of hyperparmeters is 11.

Hyperparameter 'theta1'
hyperid =

⁠106101⁠

name =

⁠shape⁠

short.name =

⁠alpha⁠

output.name.intern =

⁠alpha_intern for Gompertz-surv⁠

output.name =

⁠alpha parameter for Gompertz-surv⁠

initial =

⁠-10⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 1⁠

to.theta =

⁠function(x, sc = 0.1) log(x) / sc⁠

from.theta =

⁠function(x, sc = 0.1) exp(sc * x)⁠

Hyperparameter 'theta2'
hyperid =

⁠106102⁠

name =

⁠beta1⁠

short.name =

⁠beta1⁠

output.name =

⁠beta1 for Gompertz-Cure⁠

output.name.intern =

⁠beta1 for Gompertz-Cure⁠

initial =

⁠-5⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠-4 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠106103⁠

name =

⁠beta2⁠

short.name =

⁠beta2⁠

output.name =

⁠beta2 for Gompertz-Cure⁠

output.name.intern =

⁠beta2 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠106104⁠

name =

⁠beta3⁠

short.name =

⁠beta3⁠

output.name =

⁠beta3 for Gompertz-Cure⁠

output.name.intern =

⁠beta3 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠106105⁠

name =

⁠beta4⁠

short.name =

⁠beta4⁠

output.name =

⁠beta4 for Gompertz-Cure⁠

output.name.intern =

⁠beta4 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠106106⁠

name =

⁠beta5⁠

short.name =

⁠beta5⁠

output.name =

⁠beta5 for Gompertz-Cure⁠

output.name.intern =

⁠beta5 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠106107⁠

name =

⁠beta6⁠

short.name =

⁠beta6⁠

output.name =

⁠beta6 for Gompertz-Cure⁠

output.name.intern =

⁠beta6 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠106108⁠

name =

⁠beta7⁠

short.name =

⁠beta7⁠

output.name =

⁠beta7 for Gompertz-Cure⁠

output.name.intern =

⁠beta7 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠106109⁠

name =

⁠beta8⁠

short.name =

⁠beta8⁠

output.name =

⁠beta8 for Gompertz-Cure⁠

output.name.intern =

⁠beta8 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠106110⁠

name =

⁠beta9⁠

short.name =

⁠beta9⁠

output.name =

⁠beta9 for Gompertz-Cure⁠

output.name.intern =

⁠beta9 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠106111⁠

name =

⁠beta10⁠

short.name =

⁠beta10⁠

output.name =

⁠beta10 for Gompertz-Cure⁠

output.name.intern =

⁠beta10 for Gompertz-Cure⁠

initial =

⁠0⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 100⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'dgompertzsurv'.
Properties:
doc =

⁠destructive gompertz (survival) distribution⁠

experimental =

⁠TRUE⁠

survival =

⁠TRUE⁠

discrete =

⁠FALSE⁠

link =

⁠default log neglog⁠

pdf =

⁠dgompertz⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠108101⁠

name =

⁠shape⁠

short.name =

⁠alpha⁠

output.name.intern =

⁠alpha_intern for dGompertz⁠

output.name =

⁠alpha parameter for dGompertz⁠

initial =

⁠-1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠0 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Model 'vm'.
Properties:
doc =

⁠von Mises circular distribution⁠

experimental =

⁠TRUE⁠

survival =

⁠FALSE⁠

discrete =

⁠FALSE⁠

link =

⁠default circular tan tan.pi identity⁠

pdf =

⁠vm⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠109101⁠

name =

⁠precision⁠

short.name =

⁠prec⁠

output.name.intern =

⁠prec_intern for vm⁠

output.name =

⁠precision parameter for vm⁠

initial =

⁠2⁠

fixed =

⁠FALSE⁠

prior =

⁠loggamma⁠

param =

⁠1 0.01⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'prior'

Valid models in this section are:

Model 'normal'.

Number of parameters in the prior = 2

Model 'gaussian'.

Number of parameters in the prior = 2

Model 'laplace'.

Number of parameters in the prior = 2

Model 'linksnintercept'.

Number of parameters in the prior = 2

Model 'wishart1d'.

Number of parameters in the prior = 2

Model 'wishart2d'.

Number of parameters in the prior = 4

Model 'wishart3d'.

Number of parameters in the prior = 7

Model 'wishart4d'.

Number of parameters in the prior = 11

Model 'wishart5d'.

Number of parameters in the prior = 16

Model 'loggamma'.

Number of parameters in the prior = 2

Model 'gamma'.

Number of parameters in the prior = 2

Model 'minuslogsqrtruncnormal'.

Number of parameters in the prior = 2

Model 'logtnormal'.

Number of parameters in the prior = 2

Model 'logtgaussian'.

Number of parameters in the prior = 2

Model 'flat'.

Number of parameters in the prior = 0

Model 'logflat'.

Number of parameters in the prior = 0

Model 'logiflat'.

Number of parameters in the prior = 0

Model 'mvnorm'.

Number of parameters in the prior = -1

Model 'pc.alphaw'.

Number of parameters in the prior = 1

Model 'pc.ar'.

Number of parameters in the prior = 1

Model 'dirichlet'.

Number of parameters in the prior = 1

Model 'none'.

Number of parameters in the prior = 0

Model 'invalid'.

Number of parameters in the prior = 0

Model 'betacorrelation'.

Number of parameters in the prior = 2

Model 'logitbeta'.

Number of parameters in the prior = 2

Model 'pc.prec'.

Number of parameters in the prior = 2

Model 'pc.dof'.

Number of parameters in the prior = 2

Model 'pc.cor0'.

Number of parameters in the prior = 2

Model 'pc.cor1'.

Number of parameters in the prior = 2

Model 'pc.fgnh'.

Number of parameters in the prior = 2

Model 'pc.spde.GA'.

Number of parameters in the prior = 4

Model 'pc.matern'.

Number of parameters in the prior = 3

Model 'pc.range'.

Number of parameters in the prior = 2

Model 'pc.sn'.

Number of parameters in the prior = 1

Model 'pc.gamma'.

Number of parameters in the prior = 1

Model 'pc.mgamma'.

Number of parameters in the prior = 1

Model 'pc.gammacount'.

Number of parameters in the prior = 1

Model 'pc.gevtail'.

Number of parameters in the prior = 3

Model 'pc.egptail'.

Number of parameters in the prior = 3

Model 'pc'.

Number of parameters in the prior = 2

Model 'ref.ar'.

Number of parameters in the prior = 0

Model 'pom'.

Number of parameters in the prior = 0

Model 'jeffreystdf'.

Number of parameters in the prior = 0

Model 'wishartkd'.

Number of parameters in the prior = 301

Model 'expression:'.

Number of parameters in the prior = -1

Model 'table:'.

Number of parameters in the prior = -1

Model 'rprior:'.

Number of parameters in the prior = 0

'wrapper'

Valid models in this section are:

Model 'joint'.
Properties:
doc =

⁠(experimental)⁠

constr =

⁠FALSE⁠

nrow.ncol =

⁠FALSE⁠

augmented =

⁠FALSE⁠

aug.factor =

⁠1⁠

aug.constr =

⁠NULL⁠

n.div.by =

⁠NULL⁠

n.required =

⁠FALSE⁠

set.default.values =

⁠FALSE⁠

pdf =

⁠NA⁠

Number of hyperparmeters is 1.

Hyperparameter 'theta'
hyperid =

⁠102001⁠

name =

⁠log precision⁠

short.name =

⁠prec⁠

output.name =

⁠NOT IN USE⁠

output.name.intern =

⁠NOT IN USE⁠

initial =

⁠0⁠

fixed =

⁠TRUE⁠

prior =

⁠loggamma⁠

param =

⁠1 5e-05⁠

to.theta =

⁠function(x) log(x)⁠

from.theta =

⁠function(x) exp(x)⁠

'lp.scale'

Valid models in this section are:

Model 'lp.scale'.
Properties:
pdf =

⁠lp.scale⁠

Number of hyperparmeters is 100.

Hyperparameter 'theta1'
hyperid =

⁠103001⁠

name =

⁠beta1⁠

short.name =

⁠b1⁠

output.name =

⁠beta[1] for lp_scale⁠

output.name.intern =

⁠beta[1] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta2'
hyperid =

⁠103002⁠

name =

⁠beta2⁠

short.name =

⁠b2⁠

output.name =

⁠beta[2] for lp_scale⁠

output.name.intern =

⁠beta[2] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta3'
hyperid =

⁠103003⁠

name =

⁠beta3⁠

short.name =

⁠b3⁠

output.name =

⁠beta[3] for lp_scale⁠

output.name.intern =

⁠beta[3] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta4'
hyperid =

⁠103004⁠

name =

⁠beta4⁠

short.name =

⁠b4⁠

output.name =

⁠beta[4] for lp_scale⁠

output.name.intern =

⁠beta[4] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta5'
hyperid =

⁠103005⁠

name =

⁠beta5⁠

short.name =

⁠b5⁠

output.name =

⁠beta[5] for lp_scale⁠

output.name.intern =

⁠beta[5] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta6'
hyperid =

⁠103006⁠

name =

⁠beta6⁠

short.name =

⁠b6⁠

output.name =

⁠beta[6] for lp_scale⁠

output.name.intern =

⁠beta[6] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta7'
hyperid =

⁠103007⁠

name =

⁠beta7⁠

short.name =

⁠b7⁠

output.name =

⁠beta[7] for lp_scale⁠

output.name.intern =

⁠beta[7] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta8'
hyperid =

⁠103008⁠

name =

⁠beta8⁠

short.name =

⁠b8⁠

output.name =

⁠beta[8] for lp_scale⁠

output.name.intern =

⁠beta[8] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta9'
hyperid =

⁠103009⁠

name =

⁠beta9⁠

short.name =

⁠b9⁠

output.name =

⁠beta[9] for lp_scale⁠

output.name.intern =

⁠beta[9] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta10'
hyperid =

⁠103010⁠

name =

⁠beta10⁠

short.name =

⁠b10⁠

output.name =

⁠beta[10] for lp_scale⁠

output.name.intern =

⁠beta[10] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta11'
hyperid =

⁠103011⁠

name =

⁠beta11⁠

short.name =

⁠b11⁠

output.name =

⁠beta[11] for lp_scale⁠

output.name.intern =

⁠beta[11] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta12'
hyperid =

⁠103012⁠

name =

⁠beta12⁠

short.name =

⁠b12⁠

output.name =

⁠beta[12] for lp_scale⁠

output.name.intern =

⁠beta[12] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta13'
hyperid =

⁠103013⁠

name =

⁠beta13⁠

short.name =

⁠b13⁠

output.name =

⁠beta[13] for lp_scale⁠

output.name.intern =

⁠beta[13] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta14'
hyperid =

⁠103014⁠

name =

⁠beta14⁠

short.name =

⁠b14⁠

output.name =

⁠beta[14] for lp_scale⁠

output.name.intern =

⁠beta[14] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta15'
hyperid =

⁠103015⁠

name =

⁠beta15⁠

short.name =

⁠b15⁠

output.name =

⁠beta[15] for lp_scale⁠

output.name.intern =

⁠beta[15] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta16'
hyperid =

⁠103016⁠

name =

⁠beta16⁠

short.name =

⁠b16⁠

output.name =

⁠beta[16] for lp_scale⁠

output.name.intern =

⁠beta[16] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta17'
hyperid =

⁠103017⁠

name =

⁠beta17⁠

short.name =

⁠b17⁠

output.name =

⁠beta[17] for lp_scale⁠

output.name.intern =

⁠beta[17] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta18'
hyperid =

⁠103018⁠

name =

⁠beta18⁠

short.name =

⁠b18⁠

output.name =

⁠beta[18] for lp_scale⁠

output.name.intern =

⁠beta[18] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta19'
hyperid =

⁠103019⁠

name =

⁠beta19⁠

short.name =

⁠b19⁠

output.name =

⁠beta[19] for lp_scale⁠

output.name.intern =

⁠beta[19] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta20'
hyperid =

⁠103020⁠

name =

⁠beta20⁠

short.name =

⁠b20⁠

output.name =

⁠beta[20] for lp_scale⁠

output.name.intern =

⁠beta[20] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta21'
hyperid =

⁠103021⁠

name =

⁠beta21⁠

short.name =

⁠b21⁠

output.name =

⁠beta[21] for lp_scale⁠

output.name.intern =

⁠beta[21] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta22'
hyperid =

⁠103022⁠

name =

⁠beta22⁠

short.name =

⁠b22⁠

output.name =

⁠beta[22] for lp_scale⁠

output.name.intern =

⁠beta[22] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta23'
hyperid =

⁠103023⁠

name =

⁠beta23⁠

short.name =

⁠b23⁠

output.name =

⁠beta[23] for lp_scale⁠

output.name.intern =

⁠beta[23] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta24'
hyperid =

⁠103024⁠

name =

⁠beta24⁠

short.name =

⁠b24⁠

output.name =

⁠beta[24] for lp_scale⁠

output.name.intern =

⁠beta[24] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta25'
hyperid =

⁠103025⁠

name =

⁠beta25⁠

short.name =

⁠b25⁠

output.name =

⁠beta[25] for lp_scale⁠

output.name.intern =

⁠beta[25] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta26'
hyperid =

⁠103026⁠

name =

⁠beta26⁠

short.name =

⁠b26⁠

output.name =

⁠beta[26] for lp_scale⁠

output.name.intern =

⁠beta[26] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta27'
hyperid =

⁠103027⁠

name =

⁠beta27⁠

short.name =

⁠b27⁠

output.name =

⁠beta[27] for lp_scale⁠

output.name.intern =

⁠beta[27] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta28'
hyperid =

⁠103028⁠

name =

⁠beta28⁠

short.name =

⁠b28⁠

output.name =

⁠beta[28] for lp_scale⁠

output.name.intern =

⁠beta[28] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta29'
hyperid =

⁠103029⁠

name =

⁠beta29⁠

short.name =

⁠b29⁠

output.name =

⁠beta[29] for lp_scale⁠

output.name.intern =

⁠beta[29] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta30'
hyperid =

⁠103030⁠

name =

⁠beta30⁠

short.name =

⁠b30⁠

output.name =

⁠beta[30] for lp_scale⁠

output.name.intern =

⁠beta[30] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta31'
hyperid =

⁠103031⁠

name =

⁠beta31⁠

short.name =

⁠b31⁠

output.name =

⁠beta[31] for lp_scale⁠

output.name.intern =

⁠beta[31] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta32'
hyperid =

⁠103032⁠

name =

⁠beta32⁠

short.name =

⁠b32⁠

output.name =

⁠beta[32] for lp_scale⁠

output.name.intern =

⁠beta[32] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta33'
hyperid =

⁠103033⁠

name =

⁠beta33⁠

short.name =

⁠b33⁠

output.name =

⁠beta[33] for lp_scale⁠

output.name.intern =

⁠beta[33] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta34'
hyperid =

⁠103034⁠

name =

⁠beta34⁠

short.name =

⁠b34⁠

output.name =

⁠beta[34] for lp_scale⁠

output.name.intern =

⁠beta[34] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta35'
hyperid =

⁠103035⁠

name =

⁠beta35⁠

short.name =

⁠b35⁠

output.name =

⁠beta[35] for lp_scale⁠

output.name.intern =

⁠beta[35] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta36'
hyperid =

⁠103036⁠

name =

⁠beta36⁠

short.name =

⁠b36⁠

output.name =

⁠beta[36] for lp_scale⁠

output.name.intern =

⁠beta[36] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta37'
hyperid =

⁠103037⁠

name =

⁠beta37⁠

short.name =

⁠b37⁠

output.name =

⁠beta[37] for lp_scale⁠

output.name.intern =

⁠beta[37] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta38'
hyperid =

⁠103038⁠

name =

⁠beta38⁠

short.name =

⁠b38⁠

output.name =

⁠beta[38] for lp_scale⁠

output.name.intern =

⁠beta[38] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta39'
hyperid =

⁠103039⁠

name =

⁠beta39⁠

short.name =

⁠b39⁠

output.name =

⁠beta[39] for lp_scale⁠

output.name.intern =

⁠beta[39] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta40'
hyperid =

⁠103040⁠

name =

⁠beta40⁠

short.name =

⁠b40⁠

output.name =

⁠beta[40] for lp_scale⁠

output.name.intern =

⁠beta[40] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta41'
hyperid =

⁠103041⁠

name =

⁠beta41⁠

short.name =

⁠b41⁠

output.name =

⁠beta[41] for lp_scale⁠

output.name.intern =

⁠beta[41] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta42'
hyperid =

⁠103042⁠

name =

⁠beta42⁠

short.name =

⁠b42⁠

output.name =

⁠beta[42] for lp_scale⁠

output.name.intern =

⁠beta[42] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta43'
hyperid =

⁠103043⁠

name =

⁠beta43⁠

short.name =

⁠b43⁠

output.name =

⁠beta[43] for lp_scale⁠

output.name.intern =

⁠beta[43] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta44'
hyperid =

⁠103044⁠

name =

⁠beta44⁠

short.name =

⁠b44⁠

output.name =

⁠beta[44] for lp_scale⁠

output.name.intern =

⁠beta[44] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta45'
hyperid =

⁠103045⁠

name =

⁠beta45⁠

short.name =

⁠b45⁠

output.name =

⁠beta[45] for lp_scale⁠

output.name.intern =

⁠beta[45] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta46'
hyperid =

⁠103046⁠

name =

⁠beta46⁠

short.name =

⁠b46⁠

output.name =

⁠beta[46] for lp_scale⁠

output.name.intern =

⁠beta[46] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta47'
hyperid =

⁠103047⁠

name =

⁠beta47⁠

short.name =

⁠b47⁠

output.name =

⁠beta[47] for lp_scale⁠

output.name.intern =

⁠beta[47] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta48'
hyperid =

⁠103048⁠

name =

⁠beta48⁠

short.name =

⁠b48⁠

output.name =

⁠beta[48] for lp_scale⁠

output.name.intern =

⁠beta[48] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta49'
hyperid =

⁠103049⁠

name =

⁠beta49⁠

short.name =

⁠b49⁠

output.name =

⁠beta[49] for lp_scale⁠

output.name.intern =

⁠beta[49] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta50'
hyperid =

⁠103050⁠

name =

⁠beta50⁠

short.name =

⁠b50⁠

output.name =

⁠beta[50] for lp_scale⁠

output.name.intern =

⁠beta[50] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta51'
hyperid =

⁠103051⁠

name =

⁠beta51⁠

short.name =

⁠b51⁠

output.name =

⁠beta[51] for lp_scale⁠

output.name.intern =

⁠beta[51] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta52'
hyperid =

⁠103052⁠

name =

⁠beta52⁠

short.name =

⁠b52⁠

output.name =

⁠beta[52] for lp_scale⁠

output.name.intern =

⁠beta[52] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta53'
hyperid =

⁠103053⁠

name =

⁠beta53⁠

short.name =

⁠b53⁠

output.name =

⁠beta[53] for lp_scale⁠

output.name.intern =

⁠beta[53] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta54'
hyperid =

⁠103054⁠

name =

⁠beta54⁠

short.name =

⁠b54⁠

output.name =

⁠beta[54] for lp_scale⁠

output.name.intern =

⁠beta[54] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta55'
hyperid =

⁠103055⁠

name =

⁠beta55⁠

short.name =

⁠b55⁠

output.name =

⁠beta[55] for lp_scale⁠

output.name.intern =

⁠beta[55] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta56'
hyperid =

⁠103056⁠

name =

⁠beta56⁠

short.name =

⁠b56⁠

output.name =

⁠beta[56] for lp_scale⁠

output.name.intern =

⁠beta[56] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta57'
hyperid =

⁠103057⁠

name =

⁠beta57⁠

short.name =

⁠b57⁠

output.name =

⁠beta[57] for lp_scale⁠

output.name.intern =

⁠beta[57] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta58'
hyperid =

⁠103058⁠

name =

⁠beta58⁠

short.name =

⁠b58⁠

output.name =

⁠beta[58] for lp_scale⁠

output.name.intern =

⁠beta[58] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta59'
hyperid =

⁠103059⁠

name =

⁠beta59⁠

short.name =

⁠b59⁠

output.name =

⁠beta[59] for lp_scale⁠

output.name.intern =

⁠beta[59] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta60'
hyperid =

⁠103060⁠

name =

⁠beta60⁠

short.name =

⁠b60⁠

output.name =

⁠beta[60] for lp_scale⁠

output.name.intern =

⁠beta[60] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta61'
hyperid =

⁠103061⁠

name =

⁠beta61⁠

short.name =

⁠b61⁠

output.name =

⁠beta[61] for lp_scale⁠

output.name.intern =

⁠beta[61] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta62'
hyperid =

⁠103062⁠

name =

⁠beta62⁠

short.name =

⁠b62⁠

output.name =

⁠beta[62] for lp_scale⁠

output.name.intern =

⁠beta[62] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta63'
hyperid =

⁠103063⁠

name =

⁠beta63⁠

short.name =

⁠b63⁠

output.name =

⁠beta[63] for lp_scale⁠

output.name.intern =

⁠beta[63] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta64'
hyperid =

⁠103064⁠

name =

⁠beta64⁠

short.name =

⁠b64⁠

output.name =

⁠beta[64] for lp_scale⁠

output.name.intern =

⁠beta[64] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta65'
hyperid =

⁠103065⁠

name =

⁠beta65⁠

short.name =

⁠b65⁠

output.name =

⁠beta[65] for lp_scale⁠

output.name.intern =

⁠beta[65] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta66'
hyperid =

⁠103066⁠

name =

⁠beta66⁠

short.name =

⁠b66⁠

output.name =

⁠beta[66] for lp_scale⁠

output.name.intern =

⁠beta[66] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta67'
hyperid =

⁠103067⁠

name =

⁠beta67⁠

short.name =

⁠b67⁠

output.name =

⁠beta[67] for lp_scale⁠

output.name.intern =

⁠beta[67] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta68'
hyperid =

⁠103068⁠

name =

⁠beta68⁠

short.name =

⁠b68⁠

output.name =

⁠beta[68] for lp_scale⁠

output.name.intern =

⁠beta[68] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta69'
hyperid =

⁠103069⁠

name =

⁠beta69⁠

short.name =

⁠b69⁠

output.name =

⁠beta[69] for lp_scale⁠

output.name.intern =

⁠beta[69] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta70'
hyperid =

⁠103070⁠

name =

⁠beta70⁠

short.name =

⁠b70⁠

output.name =

⁠beta[70] for lp_scale⁠

output.name.intern =

⁠beta[70] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta71'
hyperid =

⁠103071⁠

name =

⁠beta71⁠

short.name =

⁠b71⁠

output.name =

⁠beta[71] for lp_scale⁠

output.name.intern =

⁠beta[71] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta72'
hyperid =

⁠103072⁠

name =

⁠beta72⁠

short.name =

⁠b72⁠

output.name =

⁠beta[72] for lp_scale⁠

output.name.intern =

⁠beta[72] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta73'
hyperid =

⁠103073⁠

name =

⁠beta73⁠

short.name =

⁠b73⁠

output.name =

⁠beta[73] for lp_scale⁠

output.name.intern =

⁠beta[73] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta74'
hyperid =

⁠103074⁠

name =

⁠beta74⁠

short.name =

⁠b74⁠

output.name =

⁠beta[74] for lp_scale⁠

output.name.intern =

⁠beta[74] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta75'
hyperid =

⁠103075⁠

name =

⁠beta75⁠

short.name =

⁠b75⁠

output.name =

⁠beta[75] for lp_scale⁠

output.name.intern =

⁠beta[75] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta76'
hyperid =

⁠103076⁠

name =

⁠beta76⁠

short.name =

⁠b76⁠

output.name =

⁠beta[76] for lp_scale⁠

output.name.intern =

⁠beta[76] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta77'
hyperid =

⁠103077⁠

name =

⁠beta77⁠

short.name =

⁠b77⁠

output.name =

⁠beta[77] for lp_scale⁠

output.name.intern =

⁠beta[77] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta78'
hyperid =

⁠103078⁠

name =

⁠beta78⁠

short.name =

⁠b78⁠

output.name =

⁠beta[78] for lp_scale⁠

output.name.intern =

⁠beta[78] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta79'
hyperid =

⁠103079⁠

name =

⁠beta79⁠

short.name =

⁠b79⁠

output.name =

⁠beta[79] for lp_scale⁠

output.name.intern =

⁠beta[79] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta80'
hyperid =

⁠103080⁠

name =

⁠beta80⁠

short.name =

⁠b80⁠

output.name =

⁠beta[80] for lp_scale⁠

output.name.intern =

⁠beta[80] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta81'
hyperid =

⁠103081⁠

name =

⁠beta81⁠

short.name =

⁠b81⁠

output.name =

⁠beta[81] for lp_scale⁠

output.name.intern =

⁠beta[81] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta82'
hyperid =

⁠103082⁠

name =

⁠beta82⁠

short.name =

⁠b82⁠

output.name =

⁠beta[82] for lp_scale⁠

output.name.intern =

⁠beta[82] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta83'
hyperid =

⁠103083⁠

name =

⁠beta83⁠

short.name =

⁠b83⁠

output.name =

⁠beta[83] for lp_scale⁠

output.name.intern =

⁠beta[83] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta84'
hyperid =

⁠103084⁠

name =

⁠beta84⁠

short.name =

⁠b84⁠

output.name =

⁠beta[84] for lp_scale⁠

output.name.intern =

⁠beta[84] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta85'
hyperid =

⁠103085⁠

name =

⁠beta85⁠

short.name =

⁠b85⁠

output.name =

⁠beta[85] for lp_scale⁠

output.name.intern =

⁠beta[85] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta86'
hyperid =

⁠103086⁠

name =

⁠beta86⁠

short.name =

⁠b86⁠

output.name =

⁠beta[86] for lp_scale⁠

output.name.intern =

⁠beta[86] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta87'
hyperid =

⁠103087⁠

name =

⁠beta87⁠

short.name =

⁠b87⁠

output.name =

⁠beta[87] for lp_scale⁠

output.name.intern =

⁠beta[87] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta88'
hyperid =

⁠103088⁠

name =

⁠beta88⁠

short.name =

⁠b88⁠

output.name =

⁠beta[88] for lp_scale⁠

output.name.intern =

⁠beta[88] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta89'
hyperid =

⁠103089⁠

name =

⁠beta89⁠

short.name =

⁠b89⁠

output.name =

⁠beta[89] for lp_scale⁠

output.name.intern =

⁠beta[89] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta90'
hyperid =

⁠103090⁠

name =

⁠beta90⁠

short.name =

⁠b90⁠

output.name =

⁠beta[90] for lp_scale⁠

output.name.intern =

⁠beta[90] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta91'
hyperid =

⁠103091⁠

name =

⁠beta91⁠

short.name =

⁠b91⁠

output.name =

⁠beta[91] for lp_scale⁠

output.name.intern =

⁠beta[91] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta92'
hyperid =

⁠103092⁠

name =

⁠beta92⁠

short.name =

⁠b92⁠

output.name =

⁠beta[92] for lp_scale⁠

output.name.intern =

⁠beta[92] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta93'
hyperid =

⁠103093⁠

name =

⁠beta93⁠

short.name =

⁠b93⁠

output.name =

⁠beta[93] for lp_scale⁠

output.name.intern =

⁠beta[93] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta94'
hyperid =

⁠103094⁠

name =

⁠beta94⁠

short.name =

⁠b94⁠

output.name =

⁠beta[94] for lp_scale⁠

output.name.intern =

⁠beta[94] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta95'
hyperid =

⁠103095⁠

name =

⁠beta95⁠

short.name =

⁠b95⁠

output.name =

⁠beta[95] for lp_scale⁠

output.name.intern =

⁠beta[95] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta96'
hyperid =

⁠103096⁠

name =

⁠beta96⁠

short.name =

⁠b96⁠

output.name =

⁠beta[96] for lp_scale⁠

output.name.intern =

⁠beta[96] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta97'
hyperid =

⁠103097⁠

name =

⁠beta97⁠

short.name =

⁠b97⁠

output.name =

⁠beta[97] for lp_scale⁠

output.name.intern =

⁠beta[97] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta98'
hyperid =

⁠103098⁠

name =

⁠beta98⁠

short.name =

⁠b98⁠

output.name =

⁠beta[98] for lp_scale⁠

output.name.intern =

⁠beta[98] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta99'
hyperid =

⁠103099⁠

name =

⁠beta99⁠

short.name =

⁠b99⁠

output.name =

⁠beta[99] for lp_scale⁠

output.name.intern =

⁠beta[99] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Hyperparameter 'theta100'
hyperid =

⁠103100⁠

name =

⁠beta100⁠

short.name =

⁠b100⁠

output.name =

⁠beta[100] for lp_scale⁠

output.name.intern =

⁠beta[100] for lp_scale⁠

initial =

⁠1⁠

fixed =

⁠FALSE⁠

prior =

⁠normal⁠

param =

⁠1 10⁠

to.theta =

⁠function(x) x⁠

from.theta =

⁠function(x) x⁠

Examples

## How to set hyperparameters to pass as the argument 'hyper'. This
## format is compatible with the old style (using 'initial', 'fixed',
## 'prior', 'param'), but the new style using 'hyper' takes precedence
## over the old style. The two styles can also be mixed. The old style
## might be removed from the code in the future...

## Only a subset need to be given
hyper <- list(theta = list(initial = 2))
## The `name' can be used instead of 'theta', or 'theta1', 'theta2',...
hyper <- list(precision = list(initial = 2))
hyper <- list(precision = list(prior = "flat", param = numeric(0)))
hyper <- list(theta2 = list(initial = 3), theta1 = list(prior = "gaussian"))
## The 'short.name' can be used instead of 'name'
hyper <- list(rho = list(param = c(0, 1)))

Estimate posterior distributions of fitted lambda values

Description

For use with 'nmix' and 'nmixnb' models. This function takes the information contained in an object returned by inla() and uses the contents to create fitted lambda values using the linear predictor for log(lambda), the input covariate values, and samples from the posteriors of the model hyperparameters. Fitted values from the linear predictor are exponentiated, by default, before being returned.

Usage

inla.nmix.lambda.fitted(
  result,
  sample.size = 1000,
  return.posteriors = FALSE,
  scale = "exp"
)

Arguments

result

The output object from a call to inla(), where the family argument has been set to 'nmix' or 'nmixnb'. For the function to work, the call to inla() should also include the argument ⁠control.compute=list(config = TRUE))⁠.
sample.size

The size of the sample from the posteriors of the model hyperparameters. This sample size ends up being the size of the estimated posterior for a fitted lambda value. Default is 1000. Larger values are recommended.
return.posteriors

A logical value for whether or not to return the full estimated posteriors for each fitted value (TRUE), or just a summary of the posteriors (FALSE). Default is FALSE.
scale

A character string, where the default string, "exp", causes values from the linear predictor to be exponentiated before being returned. The string, "log", causes values to be returned on the log(lambda) scale.

Value

fitted.summary

A data frame with summaries of estimated posteriors of fitted lambda values. The number of rows equals the number of rows in the data used to create the 'nmix' or 'nmixnb' model. There are six columns of summary statistics for each estimated posterior. Columns include an index, mean.lambda, sd.lambda, quant025.lambda, median.lambda, quant975.lambda, and mode.lambda.
fitted.posteriors

A data frame containing samples that comprise the full estimated posteriors of fitted values. The number of rows equals the number of rows in the data used to create the 'nmix' or 'nmixnb' model. The number of columns equals one plus the number of samples specified by the sample.size argument.

Note

This function is experimental.

Author(s)

Tim Meehan tmeehan@audubon.org

References

See documentation for families "nmix" and "nmixmb": inla.doc("nmix")

Examples

## an example analysis of an N-mixture model using simulated data
## set parameters
n <- 75                       # number of study sites
nrep.max <- 5                 # number of surveys per site
b0 <- 0.5                     # lambda intercept, expected abundance
b1 <- 2.0                     # effect of x1 on lambda
a0 <- 1.0                     # p intercept, detection probability
a2 <- 0.5                     # effect of x2 on p
size <- 3.0                   # size of theta
overdispersion <- 1 / size    # for negative binomial distribution

## make empty vectors and matrix
x1 <- c(); x2 <- c()
lambdas <- c(); Ns <- c()
y <- matrix(NA, n, nrep.max)

## fill vectors and matrix
for(i in 1:n) {
    x1.i <- runif(1) - 0.5
    lambda <- exp(b0 + b1 * x1.i)
    N <- rnbinom(1, mu = lambda, size = size)
    x2.i <- runif(1) - 0.5
    eta <- a0 + a2 * x2.i
    p <- exp(eta) / (exp(eta) + 1)
    nr <- sample(1:nrep.max, 1)
    y[i, 1:nr] <- rbinom(nr, size = N, prob = p)
    x1 <- c(x1, x1.i); x2 <- c(x2, x2.i)
    lambdas <- c(lambdas, lambda); Ns <- c(Ns, N)
}

## bundle counts, lambda intercept, and lambda covariates
Y <- inla.mdata(y, 1, x1)

## run inla and summarize output
result <- inla(Y ~ 1 + x2,
  data = list(Y=Y, x2=x2),
  family = "nmixnb",
  control.fixed = list(mean = 0, mean.intercept = 0, prec = 0.01,
                      prec.intercept = 0.01),
  control.family = list(hyper = list(theta1 = list(param = c(0, 0.01)),
                                    theta2 = list(param = c(0, 0.01)),
                                    theta3 = list(prior = "flat",
                                                 param = numeric()))),
  control.compute=list(config = TRUE)) # important argument
summary(result)

## get and evaluate fitted values
lam.fits <- inla.nmix.lambda.fitted(result, 5000)$fitted.summary
plot(lam.fits$median.lambda, lambdas)
round(sum(lam.fits$median.lambda), 0); sum(Ns)

Nonconvex set extensions.

Description

[Deprecated] Use fmesher::fm_nonconvex_hull_inla() or fmesher::fm_nonconvex_hull() instead.

Constructs a nonconvex boundary for a point set using morphological operations.

Usage

inla.nonconvex.hull(
  points,
  convex = -0.15,
  concave = convex,
  resolution = 40,
  eps = NULL,
  crs = NULL
)

inla.nonconvex.hull.basic(
  points,
  convex = -0.15,
  resolution = 40,
  eps = NULL,
  crs = NULL
)

Arguments

points

2D point coordinates (2-column matrix). Can alternatively be a SpatialPoints or SpatialPointsDataFrame object.
convex

The desired extension radius. Also determines the smallest allowed convex curvature radius. Negative values are interpreted as fractions of the approximate initial set diameter.
concave

The desired minimal concave curvature radius. Default is concave=convex.
resolution

The internal computation resolution. A warning will be issued when this needs to be increased for higher accuracy, with the required resolution stated.
eps

The polygonal curve simplification tolerance used for simplifying the resulting boundary curve. See inla.simplify.curve() for details.
crs

An optional CRS or inla.CRS object

Details

Morphological dilation by convex, followed by closing by concave, with minimum concave curvature radius concave. If the dilated set has no gaps of width between

2convex(1+2concave/convex−1)2 convex (\sqrt{1+2 concave/convex} - 1)

and 2concave2 concave, then the minimum convex curvature radius is convex. Special case concave=0 delegates to inla.nonconvex.hull.basic

The implementation is based on the identity

dilation(a)&closing(b)=dilation(a+b)&erosion(b)dilation(a) \& closing(b) = dilation(a+b) \& erosion(b)

where all operations are with respect to disks with the specified radii.

Value

An inla.mesh.segment() object.

Functions

Note

Requires nndistF from the splancs package.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Examples

if (require(splancs)) {
  loc <- matrix(runif(20), 10, 2)
  boundary <- inla.nonconvex.hull(loc, convex = 0.2)
  lines(boundary, add = FALSE)
  points(loc)
}

Set and get global options for INLA

Description

Set and get global options for INLA

Usage

inla.getOption(
  option = c("inla.call", "inla.arg", "fmesher.call", "fmesher.arg", "num.threads",
    "smtp", "safe", "keep", "verbose", "save.memory", "internal.opt",
    "working.directory", "silent", "debug", "show.warning.graph.file",
    "scale.model.default", "short.summary", "inla.timeout", "fmesher.timeout",
    "inla.mode", "malloc.lib", "fmesher.evolution", "fmesher.evolution.warn",
    "fmesher.evolution.verbosity", "INLAjoint.features", "numa")
)

inla.setOption(...)

Arguments

option

The option to get. If option = NULL then inla.getOption then inla.getOption will return a named list of current values, otherwise, option must be one of

inla.call

The path to the inla-program.

inla.arg

Additional arguments to inla.call

fmesher.call

The path to the fmesher-program

fmesher.arg

Additional arguments to fmesher.call

num.threads

Character string with the number of threads to use as A:B, see ?inla

smtp

Sparse matrix library to use, one of band, taucs (default) or pardiso

safe

Run in safe-mode (ie try to automatically fix convergence errors) (default TRUE)

keep

Keep temporary files?

verbose

Verbose output?

save.memory

Save memory at the cost of (minor) accuracy and computing time?

internal.opt

Do internal online optimisations or not

working.directory

The name of the working directory.

silent

Run the inla-program in a silent mode?

debug

Run the inla-program in a debug mode?

cygwin

The home of the Cygwin installation (default "C:/cygwin") (Remote computing for Windows only) (No longer in use!)

ssh.auth.sock

The ssh bind-adress (value of $SSH_AUTH_SOCK int the Cygwin-shell). (Remote computing for Windows only)

show.warning.graph.file

Give a warning for using the obsolete argument graph.file instead of graph

scale.model.default

The default value of argument scale.model which optionally scale intrinisic models to have generalized unit average variance

short.summary

Use a less verbose output for summary. Useful for Markdown documents.

inla.timeout

The timeout limit, in whole seconds, for calls to the inla binary. Default is 0, meaning no timeout limit. Set to a positive integer to terminate inla calls if they run to long. Fractional seconds are rounded up to the nearest integer. This feature is EXPERIMENTAL and might change at a later stage.

fmesher.timeout

The timeout limit, in whole seconds, for calls to the fmesher binary. Default is 0, meaning no timeout limit. Set to a positive integer to terminate fmesher calls that may enter infinite loops due to special geometry regularity. Fractional seconds are rounded up to the nearest integer.

inla.mode

Which mode to use in INLA? Default is "compact". Other options are "classic" and "twostage".

malloc.lib

Which malloc library to use: "je", "tc", "mi", "compiler" or "default". Option "compiler" use the compiler's implementation. The library is loaded using LD_PRELOAD and similar functionality. Loosely, jemalloc is from Facebook, tcmalloc is from Google and mimalloc is from Microsoft. This option is not available for Windows and not all options might be available for every arch. If malloc.lib is a complete path to an external library, that file will be used instead of one of the supported ones.

fmesher.evolution

Control use of fmesher methods during the transition to a separate fmesher package. Levels of fmesher.evolution:

1L uses the intermediate ⁠fm_*⁠ methods in fmesher that were already available via inlabru from 2.8.0.

2L (current default) uses the full range of fmesher package methods.

Further levels may be added as the package development progresses.

fmesher.evolution.warn

logical; whether to show warnings about deprecated use of legacy INLA methods with fmesher package replacements. When TRUE, shows deprecation messages for many CRS and mesh related methods, pointing to their ⁠fm_*⁠ replacements. Default is currently FALSE.

fmesher.evolution.verbosity

logical or character; at what minimum severity to show warnings about deprecated use of legacy INLA methods with fmesher package replacements. When set to "default" (default), "soft", "warn", or "stop", indicates the minimum warning level used when fmesher.evolution.warn is TRUE.

INLAjoint.features

logical Do not use. By purpose left undocumented

numa

logical Enable NUMA features (Linux only)
...

Option and value, like option=value or ⁠option, value⁠; see the Examples

Author(s)

Havard Rue hrue@r-inla.org

Examples

## set number of threads
 inla.setOption("num.threads", "4:1")
 ## alternative format
 inla.setOption(num.threads="4:1")
 ## check it
 inla.getOption("num.threads")

Check which mesh triangles are inside a polygon

Description

Wrapper for the sp::over() method to find triangle centroids or vertices inside sp polygon objects. [Deprecated] since 23.06.06 in favour of inlabru::fm_contains() when inlabru version ⁠>= 2.7.0.9011⁠ is installed, and since 23.08.02 in favour of fmesher::fm_contains() when fmesher.

Usage

inla.over_sp_mesh(x, y, type = c("centroid", "vertex"), ignore.CRS = FALSE)

Arguments

x

geometry (typically a sp::SpatialPolygons() object) for the queries
y

an inla.mesh() object
type

the query type; either 'centroid' (default, for triangle centroids), or 'vertex' (for mesh vertices)
ignore.CRS

logical; whether to ignore the coordinate system information in x and y (default FALSE)

Value

A vector of triangle indices (when type is 'centroid') or vertex indices (when type is 'vertex')

Author(s)

Haakon Bakka, bakka@r-inla.org, and Finn Lindgren finn.lindgren@gmail.com

Examples

if (require("sp", quietly = TRUE)) {
    # Create a polygon and a mesh
    obj <- sp::SpatialPolygons(
        list(sp::Polygons(
            list(sp::Polygon(rbind(
                c(0, 0),
                c(50, 0),
                c(50, 50),
                c(0, 50)
            ))),
            ID = 1
        )),
        proj4string = fmesher::fm_CRS("longlat_globe")
    )
    mesh <- inla.mesh.create(globe = 2, crs = fmesher::fm_CRS("sphere"))

    ## 3 vertices found in the polygon
    inla.over_sp_mesh(obj, mesh, type = "vertex")

    ## 3 triangles found in the polygon
    inla.over_sp_mesh(obj, mesh)

    ## Multiple transformations can lead to slightly different results due to edge cases
    ## 4 triangles found in the polygon
    inla.over_sp_mesh(
        obj,
        fmesher::fm_transform(mesh, crs = fmesher::fm_crs("mollweide_norm"))
    )
}

Control main thread pinning for INLA (experimental)

Description

Control main thread pinning for INLA (experimental)

Usage

inla.pin()

inla.unpin()

Details

  • inla.pin set OMP variables for pinning

  • inla.unpin unset OMP variables for pinning

Value

No value is returned.

Author(s)

Havard Rue hrue@r-inla.org

Examples

inla.pin()
inla.unpin()

Print priors used

Description

Print the priors used for the hyperparameters

Usage

inla.priors.used(result, digits = 6L)

Arguments

result

An inla-object, typically the output from an inla()-call
digits

The digits argument to the function format()

Details

This function provides a more human-friendly output of result$all.hyper of all the priors used for the hyperparameters. Since not all information about the model is encoded in this object, more hyperparameters than actually used, may be printed. In particular, group.theta1 is printed even though the argument group in f() is not used. Similarly for spde-models, but the user should know that, for example, only the two first ones are actually used. Hopefully, this issue will be fixed in the future.

Author(s)

Havard Rue hrue@r-inla.org

Examples

r = inla(y ~ 1 + x, data = data.frame(y = 1:10, x = rep(1:5, 2)))
inla.priors.used(r)

Prune the INLA-package

Description

Prune the INLA-package by deleting binary files not supported by the running OS

Usage

inla.prune(ask = TRUE)

Arguments

ask

Logical. If TRUE, then ask for user confirmation before deleting. If FALSE, then delete without user confirmation.

Value

No value is returned.

Author(s)

Havard Rue hrue@r-inla.org

Control and view a remote inla-queue

Description

Control and view a remote inla-queue of submitted jobs

inla.qstat show job(s) on the server, inla.qget fetch the results (and by default remove the files on the server), inla.qdel removes a job on the server and inla.qnuke remove all jobs on the server. inla.qlog fetches the logfile only.

The recommended procedure is to use r=inla(..., inla.call="submit") and then do r=inla.qget(r) at a later stage. If the job is not finished, then r will not be overwritten and this step can be repeated. The reason for this procedure, is that some information usually stored in the result object does not go through the remote server, hence have to be appended to the results that are retrieved from the server. Hence doing r=inla(..., inla.call="submit") and then later retrive it using r=inla.qget(1), say, then r does not contain all the usual information. All the main results are there, but administrative information which is required to call inla.hyperpar or inla.rerun are not there.

Usage

## S3 method for class 'inla.q'
summary(object, ...)

## S3 method for class 'inla.q'
print(x, ...)

inla.qget(id, remove = TRUE)

inla.qdel(id)

inla.qstat(id)

inla.qlog(id)

inla.qnuke()

Arguments

object

An inla.q-object which is the output from inla.qstat
...

other arguments.
x

An inla.q-object which is the output from inla.qstat
id

The job-id which is the output from inla when the job is submitted, the job-number or job-name. For inla.qstat, id is optional and if omitted all the jobs will be listed.
remove

Logical If FALSE, leave the job on the server after retrival, otherwise remove it (default).

Value

inla.qstat returns an inla.q-object with information about current jobs.

Author(s)

Havard Rue

See Also

inla()

Examples

## Not run: 
r = inla(y~1, data = data.frame(y=rnorm(10)), inla.call="submit")
inla.qstat()
r = inla.qget(r, remove=FALSE)
inla.qdel(1)
inla.qnuke()

## End(Not run)

Reorderings methods for sparse matrices

Description

Provide the names of all implemented reordering schemes

Usage

inla.reorderings()

Value

The names of all available reorderings

Author(s)

Havard Rue hrue@r-inla.org

Examples

inla.reorderings()

Rerun an analysis

Description

Rerun inla() on an inla-object (output from link{inla})

Usage

inla.rerun(object, plain = FALSE)

Arguments

object

An inla-object, ie the output from an inla-call
plain

Logical. If FALSE (default), then make changes in object to improve the performance

Value

This function will take the result in object, and rerun inla again. If plain is FALSE, start the optimization from the mode in object so that we can obtain an improvement the mode for the hyperparameters. Otherwise, start from the same configuration as for object. The returned value is an inla-object.

See Also

inla()

Examples

r = inla(y ~ 1,  data = data.frame(y=1:10))
r = inla.rerun(r)

Generate samples, and functions thereof, from an approximated posterior of a fitted model

Description

This function generate samples, and functions of those, from an approximated posterior of a fitted model (an inla-object)

The hyperparameters are sampled from the configurations used to do the numerical integration, hence if you want a higher resolution, you need to to change the int.stratey variable and friends. The latent field is sampled from the Gaussian approximation conditioned on the hyperparameters, but with a correction for the mean (default), and optional (and by default) corrected for the estimated skewness.

The log.density report is only correct when there is no constraints. With constraints, it correct the Gaussian part of the sample for the constraints.

After the sample is (optional) skewness corrected, the log.density is is not exact for correcting for constraints, but the error is very small in most cases.

Usage

inla.posterior.sample(
  n = 1L,
  result,
  selection = list(),
  intern = FALSE,
  use.improved.mean = TRUE,
  skew.corr = TRUE,
  add.names = TRUE,
  seed = 0L,
  num.threads = NULL,
  parallel.configs = TRUE,
  verbose = FALSE
)

inla.posterior.sample.eval(fun, samples, return.matrix = TRUE, ...)

Arguments

n

Number of samples.
result

The inla-object, ie the output from an inla-call. The inla-object must be created with control.compute=list(config=TRUE).
selection

Select what part of the sample to return. By default, the whole sample is returned. selection is a named list with the name of the components of the sample, and what indices of them to return. Names include APredictor, Predictor, (Intercept), and otherwise names in the formula. The values of the list, is interpreted as indices. If they are negative, they are interpreted as 'not', a zero is interpreted as 'all', and positive indices are interpreted as 'only'. The names of elements of each samples refer to the indices in the full sample. NOTE THAT USING THIS ARGUMENT WILL MAKE inla.posterior.sample.eval FAIL.
intern

Logical. If TRUE then produce samples in the internal scale for the hyperparmater, if FALSE then produce samples in the user-scale. (For example log-precision (intern) and precision (user-scale))
use.improved.mean

Logical. If TRUE then use the marginal mean values when constructing samples. If FALSE then use the mean in the Gaussian approximations.
skew.corr

Logical. If TRUE then correct samples for skewness, if FALSE, do not correct samples for skewness (ie use the Gaussian).
add.names

Logical. If TRUE then add name for each elements of each sample. If FALSE, only add name for the first sample. (This save space.)
seed

See the same argument in ?inla.qsample for further information. In order to produce reproducible results, you ALSO need to make sure the RNG in R is in the same state, see example below. When seed is non-zero, num.threads is forced to "1:1" and parallel.configs is set to FALSE, since parallel sampling would not produce a reproducible sequence of pseudo-random numbers.
num.threads

The number of threads to use in the format 'A:B' defining the number threads in the outer (A) and inner (B) layer for nested parallelism. A '0' will be replaced intelligently. seed!=0 requires serial comptuations.
parallel.configs

Logical. If TRUE and not on Windows, then try to run each configuration in parallel (not Windows) using A threads (see num.threads), where each of them is using B:0 threads.
verbose

Logical. Run in verbose mode or not.
fun

The function to evaluate for each sample. Upon entry, the variable names defined in the model are defined as the value of the sample. The list of names are defined in result$misc$configs$contents where result is an inla-object. This includes predefined names for for the linear predictor (Predictor and APredictor), and the intercept ((Intercept) or Intercept). The hyperparameters are defined as theta, no matter if they are in the internal scale or not. The function fun can also return a vector. To simplify usage, fun can also be a vector character's. In this case fun it is interpreted as (strict) variable names, and a function is created that return these variables: if argument fun equals c("Intercept", "a[1:2]"), then this is equivalent to pass function() return(c(get('Intercept'), get('a[1:2]'))).
samples

samples is the output from inla.posterior.sample()
return.matrix

Logical. If TRUE, then return the samples of fun as matrix, otherwise, as a list.
...

Additional arguments to fun

Value

inla.posterior.sample returns a list of the samples, where each sample is a list with names hyperpar and latent, and with their marginal densities in logdens$hyperpar and logdens$latent and the joint density is in logdens$joint. inla.posterior.sample.eval return a list or a matrix of fun applied to each sample.

Author(s)

Havard Rue hrue@r-inla.org and Cristian Chiuchiolo cristian.chiuchiolo@kaust.edu.sa

Examples

r = inla(y ~ 1 ,data = data.frame(y=rnorm(1)), control.compute = list(config=TRUE))
  samples = inla.posterior.sample(2,r)

  ## reproducible results:
  inla.seed = as.integer(runif(1)*.Machine$integer.max)
  set.seed(12345)
  x = inla.posterior.sample(10, r, seed = inla.seed, num.threads="1:1")
  set.seed(12345)
  xx = inla.posterior.sample(10, r, seed = inla.seed, num.threads="1.1")
  all.equal(x, xx)

 set.seed(1234)
 n = 25
 xx = rnorm(n)
 yy = rev(xx)
 z = runif(n)
 y = rnorm(n)
 r = inla(y ~ 1 + z + f(xx) + f(yy, copy="xx"),
         data = data.frame(y, z, xx, yy),
         control.compute = list(config=TRUE),
         family = "gaussian")
 r.samples = inla.posterior.sample(10, r)

 fun = function(...) {
     mean(xx) - mean(yy)
 }
 f1 = inla.posterior.sample.eval(fun, r.samples)

 fun = function(...) {
     c(exp(Intercept), exp(Intercept + z))
 }
 f2 = inla.posterior.sample.eval(fun, r.samples)

 fun = function(...) {
     return (theta[1]/(theta[1] + theta[2]))
 }
 f3 = inla.posterior.sample.eval(fun, r.samples)

 ## Predicting nz new observations, and
 ## comparing the estimated one with the true one
 set.seed(1234)
 n = 100
 alpha = beta = s = 1
 z = rnorm(n)
 y = alpha + beta * z + rnorm(n, sd = s)
 r = inla(y ~ 1 + z,
         data = data.frame(y, z),
         control.compute = list(config=TRUE),
         family = "gaussian")
 r.samples = inla.posterior.sample(10^3, r)

 ## just return samples of the intercept
 intercepts = inla.posterior.sample.eval("Intercept", r.samples)

 nz = 3
 znew = rnorm(nz)
 fun = function(zz = NA) {
     ## theta[1] is the precision
     return (Intercept + z * zz +
             rnorm(length(zz), sd = sqrt(1/theta[1])))
 }
 par(mfrow=c(1, nz))
 f1 = inla.posterior.sample.eval(fun, r.samples, zz = znew)
 for(i in 1:nz) {
     hist(f1[i, ], n = 100, prob = TRUE)
     m = alpha + beta * znew[i]
     xx = seq(m-4*s, m+4*s, by = s/100)
     lines(xx, dnorm(xx, mean=m, sd = s), lwd=2)
 }

 ## 
 ## Be aware that using non-clean variable names might be a little tricky
 ## 
 n <- 100
 X <- matrix(rnorm(n^2), n, 2)
 x <- X[, 1]
 xx <- X[, 2]
 xxx <- x*xx
 
 y <- 1 + 2*x + 3*xx + 4*xxx + rnorm(n, sd = 0.01)
 
 r <- inla(y ~ X[, 1]*X[, 2],
           data = list(y = y, X = X),
           control.compute = list(config = TRUE))
 print(round(dig = 4, r$summary.fixed[,"mean"]))
 
 sam <- inla.posterior.sample(100, r)
 sam.extract <- inla.posterior.sample.eval(
     (function(...) {
         beta.1 <- get("X[, 1]")
         beta.2 <- get("X[, 2]")
         beta.12 <- get("X[, 1]:X[, 2]")
         return(c(Intercept, beta.1, beta.2, beta.12))
     }), sam)
 print(round(dig = 4, rowMeans(sam.extract)))
 
 ## a simpler form can also be used here, and in the examples below
 sam.extract <- inla.posterior.sample.eval(
                c("Intercept", "X[, 1]", "X[, 2]", "X[, 1]:X[, 2]"), sam)
 print(round(dig = 4, rowMeans(sam.extract)))

 r <- inla(y ~ x + xx + xxx,
           data = list(y = y, x = x, xx = xx, xxx = xxx), 
           control.compute = list(config = TRUE))
 
 sam <- inla.posterior.sample(100, r)
 sam.extract <- inla.posterior.sample.eval(
     (function(...) {
         return(c(Intercept, x, xx, xxx))
     }), sam)
 print(round(dig = 4, rowMeans(sam.extract)))

 sam.extract <- inla.posterior.sample.eval(c("Intercept", "x", "xx", "xxx"), sam)
 print(round(dig = 4, rowMeans(sam.extract)))

 r <- inla(y ~ x*xx,
           data = list(y = y, x = x, xx = xx), 
           control.compute = list(config = TRUE))
 
 sam <- inla.posterior.sample(100, r)
 sam.extract <- inla.posterior.sample.eval(
     (function(...) {
         return(c(Intercept, x, xx, get("x:xx")))
     }), sam)
 print(round(dig = 4, rowMeans(sam.extract)))

 sam.extract <- inla.posterior.sample.eval(c("Intercept", "x", "xx", "x:xx"), sam)
 print(round(dig = 4, rowMeans(sam.extract)))

Recursive curve simplification.

Description

[Deprecated] Use fmesher::fm_simplify_helper() instead.

Attempts to simplify a polygonal curve by joining nearly colinear segments.

Uses a variation of the binary splitting Ramer-Douglas-Peucker algorithm, with a width eps ellipse instead of a rectangle, motivated by prediction ellipse for Brownian bridge.

Usage

inla.simplify.curve(loc, idx, eps)

Arguments

loc

Coordinate matrix.
idx

Index vector into loc specifying a polygonal curve.
eps

Straightness tolerance.

Value

An index vector into loc specifying the simplified polygonal curve.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Examples

theta <- seq(0, 2 * pi, length.out = 1000)
loc <- cbind(cos(theta), sin(theta))
idx <- inla.simplify.curve(loc = loc, idx = 1:nrow(loc), eps = 0.01)
print(c(nrow(loc), length(idx)))
plot(loc, type = "l")
lines(loc[idx, ], col = "red")

Extract CRS information

Description

[Deprecated] Use fmesher::fm_CRS() instead.

Wrapper for CRS(projargs) (PROJ4) and CRS(wkt) for sp::Spatial objects.

This function is a convenience method to workaround PROJ4/PROJ6 differences, and the lack of a crs extraction method for Spatial objects.

Usage

inla.sp_get_crs(x)

Arguments

x

A sp::Spatial object

Value

A CRS object, or NULL if no valid CRS identified

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Examples

## Not run: 
if (require("sp", quietly = TRUE) && interactive()) {
    s <- sp::SpatialPoints(matrix(1:6, 3, 2), proj4string = fmesher::fm_CRS("sphere"))
    inla.sp_get_crs(s)
}

## End(Not run)

Observation/prediction matrices for mesh models.

Description

Constructs observation/prediction weight matrices for models based on inla.mesh() and inla.mesh.1d() objects.

For a more modular approach, see fmesher::fm_basis(), fmesher::fm_row_kron(), fmesher::fm_block(), and the inlabru bru_mapper() system.

Usage

inla.spde.make.A(
  mesh = NULL,
  loc = NULL,
  index = NULL,
  group = NULL,
  repl = 1L,
  n.spde = NULL,
  n.group = NULL,
  n.repl = NULL,
  group.mesh = NULL,
  weights = NULL,
  A.loc = NULL,
  A.group = NULL,
  group.index = NULL,
  block = NULL,
  n.block = NULL,
  block.rescale = c("none", "count", "weights", "sum"),
  ...
)

Arguments

mesh

An inla.mesh() or inla.mesh.1d() object specifying a function basis on a mesh domain. Alternatively, an inla.spde object that includes a mesh (e.g. from inla.spde2.matern()).
loc

Observation/prediction coordinates. mesh and loc defines a matrix A.loc of mapping weights between basis function weights and field values. If loc is NULL, A.loc is defined as Diagonal(n.spde, 1).
index

For each observation/prediction value, an index into loc. Default is seq_len(nrow(A.loc)).
group

For each observation/prediction value, an index into the group model.
repl

For each observation/prediction value, the replicate index.
n.spde

The number of basis functions in the mesh model. (Note: may be different than the number of mesh vertices/nodes/knots.)
n.group

The size of the group model.
n.repl

The total number of replicates.
group.mesh

An optional inla.mesh.1d() object for the group model.
weights

Optional scaling weights to be applied row-wise to the resulting matrix.
A.loc

Optional precomputed observation/prediction matrix. A.loc can be specified instead of mesh+loc, optionally with index supplied.
A.group

Optional precomputed observation/prediction matrix for the group model. A.group can be specified instead of group and/or group.mesh, optionally with group.index supplied.
group.index

For each observation/prediction value, an index into the rows of A.group.
block

Optional indices specifying block groupings: Entries with the same block value are joined into a single row in the resulting matrix, and the block values are the row indices. This is intended for construction of approximate integration schemes for regional data problems. See inla.spde.make.block.A() for details.
n.block

The number of blocks.
block.rescale

Specifies what scaling method should be used when joining entries as grouped by a block specification. See inla.spde.make.block.A() for details.
...

Additional parameters. Currently unused.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde.make.index()

Examples

loc <- matrix(runif(10000 * 2) * 1000, 10000, 2)
mesh <- inla.mesh.2d(
    loc = loc,
    cutoff = 50,
    max.edge = c(50, 500)
)
A <- inla.spde.make.A(mesh, loc = loc)

Observation matrices for mesh models.

Description

Constructs observation/prediction weight matrices for numerical integration schemes for regional data problems. Primarily intended for internal use by inla.spde.make.A().

Usage

inla.spde.make.block.A(
  A,
  block,
  n.block = max(block),
  weights = NULL,
  rescale = c("none", "count", "weights", "sum")
)

Arguments

A

A precomputed observation/prediction matrix for locations that are to be joined.
block

Indices specifying block groupings: Entries with the same block value are joined into a single row in the resulting matrix, and the block values are the row indices.
n.block

The number of blocks.
weights

Optional scaling weights to be applied row-wise to the input A matrix.
rescale

Specifies what scaling method should be used when joining the rows of the A matrix as grouped by the block specification.

  • 'none': Straight sum, no rescaling.

  • 'count': Divide by the number of entries in the block.

  • 'weights': Divide by the sum of the weight values within each block.

  • 'sum': Divide by the resulting row sums.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde.make.A()

SPDE model index vector generation

Description

Generates a list of named index vectors for an SPDE model.

Usage

inla.spde.make.index(name, n.spde, n.group = 1, n.repl = 1, ...)

Arguments

name

A character string with the base name of the effect.
n.spde

The size of the model, typically from spde$n.spde.
n.group

The size of the group model.
n.repl

The number of model replicates.
...

Additional parameters. Currently unused.

Value

A list of named index vectors.

name

Indices into the vector of latent variables
name.group

'group' indices
name.repl

Indices for replicates

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde.make.A(), inla.spde2.result()

Examples

loc <- matrix(runif(100 * 2), 100, 2)
mesh <- fmesher::fm_mesh_2d_inla(loc.domain = loc, max.edge = c(0.1, 0.5))
spde <- inla.spde2.matern(mesh)
index <- inla.spde.make.index("spatial", spde$n.spde, n.repl = 2)
spatial.A <- inla.spde.make.A(mesh, loc,
    index = rep(1:nrow(loc), 2),
    repl = rep(1:2, each = nrow(loc))
)
y <- 10 + rnorm(100 * 2)
stack <- inla.stack(
    data = list(y = y),
    A = list(spatial.A),
    effects = list(c(index, list(intercept = 1))),
    tag = "tag"
)
data <- inla.stack.data(stack, spde = spde)
formula <- y ~ -1 + intercept + f(spatial,
    model = spde,
    replicate = spatial.repl
)
result <- inla(formula,
    family = "gaussian", data = data,
    control.predictor = list(A = inla.stack.A(stack))
)
spde.result <- inla.spde2.result(result, "spatial", spde)

List SPDE models supported by inla.spde objects

Description

List SPDE models supported by inla.spde objects

Usage

inla.spde.models(function.names = FALSE)

inla.spde1.models()

inla.spde2.models()

Arguments

function.names

If FALSE, return list model name lists. If TRUE, return list of model object constructor function names.

Details

Returns a list of available SPDE model type name lists, one for each inla.spde model class (currently inla.spde1() and inla.spde2()).

Value

List of available SPDE model type name lists.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Examples

## Not run: 
## Display help for each supported inla.spde2 model:
for (model in inla.spde2.models()) {
    print(help(paste("inla.spde2.", model, sep = "")))
}

## Display help for each supported inla.spde* model:
models <- inla.spde.models()
for (type in names(models)) {
   for (model in models[[type]]) {
       print(help(paste("inla.", type, ".", model, sep = "")))
   }
}

## Display help for each supported inla.spde* model (equivalent to above):
for (model in inla.spde.models(function.names = TRUE)) {
    print(help(model))
}

## End(Not run)

Precision matrices for SPDE models

Description

Calculates the precision matrix for given parameter values based on an inla.spde model object.

Usage

inla.spde.precision(...)

inla.spde1.precision(spde, ...)

## S3 method for class 'inla.spde1'
inla.spde.precision(spde, ...)

inla.spde2.precision(
  spde,
  theta = NULL,
  phi0 = inla.spde2.theta2phi0(spde, theta),
  phi1 = inla.spde2.theta2phi1(spde, theta),
  phi2 = inla.spde2.theta2phi2(spde, theta),
  ...
)

## S3 method for class 'inla.spde2'
inla.spde.precision(
  spde,
  theta = NULL,
  phi0 = inla.spde2.theta2phi0(spde, theta),
  phi1 = inla.spde2.theta2phi1(spde, theta),
  phi2 = inla.spde2.theta2phi2(spde, theta),
  ...
)

Arguments

...

Additional parameters passed on to other methods.
spde

An inla.spde object.
theta

The parameter vector.
phi0

Internal parameter for a generic model. Expert option only.
phi1

Internal parameter for a generic model. Expert option only.
phi2

Internal parameter for a generic model. Expert option only.

Value

A sparse precision matrix.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde.models(), inla.spde2.generic(), inla.spde2.theta2phi0(), inla.spde2.theta2phi1(), inla.spde2.theta2phi2()

SPDE result extraction from INLA estimation results

Description

Exctract field and parameter values and distributions for an inla.spde SPDE effect from an inla result object.

Usage

inla.spde.result(...)

inla.spde1.result(inla, name, spde, do.transform = TRUE, ...)

## S3 method for class 'inla.spde1'
inla.spde.result(inla, name, spde, do.transform = TRUE, ...)

inla.spde2.result(inla, name, spde, do.transform = TRUE, ...)

## S3 method for class 'inla.spde2'
inla.spde.result(inla, name, spde, do.transform = TRUE, ...)

Arguments

...

Further arguments passed to and from other methods.
inla

An inla object obtained from a call to inla()
name

A character string with the name of the SPDE effect in the inla formula.
spde

The inla.spde object used for the effect in the inla formula. (Note: this could have been stored in the inla output, but isn't.) Usually the result of a call to inla.spde2.matern().
do.transform

If TRUE, also calculate marginals transformed to user-scale. Setting to FALSE is useful for large non-stationary models, as transforming many marginal densities is time-consuming.

Value

For inla.spde2 models, a list, where the nominal range and variance are defined as the values that would have been obtained with a stationary model and no boundary effects:

marginals.kappa

Marginal densities for kappa
marginals.log.kappa

Marginal densities for log(kappa)
marginals.log.range.nominal

Marginal densities for log(range)
marginals.log.tau

Marginal densities for log(tau)
marginals.log.variance.nominal

Marginal densities for log(variance)
marginals.range.nominal

Marginal densities for range
marginals.tau

Marginal densities for tau
marginals.theta

Marginal densities for the theta parameters
marginals.values

Marginal densities for the field values
marginals.variance.nominal

Marginal densities for variance
summary.hyperpar

The SPDE related part of the inla hyperpar output summary
summary.log.kappa

Summary statistics for log(kappa)
summary.log.range.nominal

Summary statistics for log(range)
summary.log.tau

Summary statistics for log(tau)
summary.log.variance.nominal

Summary statistics for log(kappa)
summary.theta

Summary statistics for the theta parameters
summary.values

Summary statistics for the field values

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde.models(), inla.spde2.matern()

Examples

loc <- matrix(runif(100 * 2), 100, 2)
mesh <- fmesher::fm_mesh_2d_inla(loc.domain = loc, max.edge = c(0.1, 0.5))
spde <- inla.spde2.matern(mesh)
index <- inla.spde.make.index("spatial", mesh$n, n.repl = 2)
spatial.A <- inla.spde.make.A(mesh, loc,
    index = rep(1:nrow(loc), 2),
    repl = rep(1:2, each = nrow(loc))
)
## Toy example with no spatial correlation (range=zero)
y <- 10 + rnorm(100 * 2)
stack <- inla.stack(
    data = list(y = y),
    A = list(spatial.A),
    effects = list(c(index, list(intercept = 1))),
    tag = "tag"
)
data <- inla.stack.data(stack, spde = spde)
formula <- y ~ -1 + intercept + f(spatial,
    model = spde,
    replicate = spatial.repl
)
result <- inla(formula,
    family = "gaussian", data = data,
    control.predictor = list(A = inla.stack.A(stack))
)
spde.result <- inla.spde.result(result, "spatial", spde)
plot(spde.result$marginals.range.nominal[[1]], type = "l")

Sample from SPDE models

Description

Old methods fo sampling from a SPDE model. For new code, use inla.spde.precision() and inla.qsample() instead.

Usage

inla.spde.sample(...)

## Default S3 method:
inla.spde.sample(precision, seed = NULL, ...)

## S3 method for class 'inla.spde'
inla.spde.sample(spde, seed = NULL, ...)

Arguments

...

Parameters passed on to other methods.
precision

A precision matrix.
seed

The seed for the pseudo-random generator.
spde

An inla.spde object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde.precision(), inla.qsample()

Old SPDE model objects for INLA

Description

Create an inla.spde1 model object.

Usage

inla.spde1.create(
  mesh,
  model = c("matern", "imatern", "matern.osc"),
  param = NULL,
  ...
)

inla.spde1.matern(mesh, ...)

inla.spde1.imatern(mesh, ...)

inla.spde1.matern.osc(mesh, ...)

Arguments

mesh

The mesh to build the model on, as an inla.mesh() object.
model

The name of the model.
param

Model specific parameters.
...

Additional parameters passed on to other methods.

Details

Note: This is an old spde object format retained for backwards compatibility. Please use inla.spde2() models for new code.

This method constructs an object for SPDE models. Currently implemented:

model="matern"

(κ2(u)−Δ)α/2(τ(u)(\kappa^2(u)-\Delta)^{\alpha/2}(\tau(u)

x(u))=W(u)x(u))=W(u)

param:

  • alpha = 1 or 2

  • basis.T = Matrix of basis functions for log⁡τ(u)\log\tau(u)

  • basis.K = Matrix of basis functions for log⁡κ2(u)\log\kappa^2(u)

model="imatern"

(−Δ)α/2(τ(u)(-\Delta)^{\alpha/2}(\tau(u)

x(u))=W(u)x(u))=W(u)

param:

  • alpha = 1 or 2

  • basis.T = Matrix of basis functions for log⁡τ(u)\log\tau(u)

Value

An inla.spde1 object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde2.matern(), inla.mesh.2d(), inla.mesh.basis()

Examples

n <- 100
field.fcn <- function(loc) (10 * cos(2 * pi * 2 * (loc[, 1] + loc[, 2])))
loc <- matrix(runif(n * 2), n, 2)
## One field, 2 observations per location
idx.y <- rep(1:n, 2)
y <- field.fcn(loc[idx.y, ]) + rnorm(length(idx.y))

mesh <- inla.mesh.create(loc, refine = list(max.edge = 0.05))
spde <- inla.spde1.create(mesh, model = "matern")
data <- list(y = y, field = mesh$idx$loc[idx.y])
formula <- y ~ -1 + f(field, model = spde)
result <- inla(formula, data = data, family = "normal")

## Plot the mesh structure:
plot(mesh)

if (require(rgl)) {
    ## Plot the posterior mean:
    plot(mesh,
        rgl = TRUE,
        result$summary.random$field[, "mean"],
        color.palette = colorRampPalette(c("blue", "green", "red"))
    )
    ## Plot residual field:
    plot(mesh,
        rgl = TRUE,
        result$summary.random$field[, "mean"] - field.fcn(mesh$loc),
        color.palette = colorRampPalette(c("blue", "green", "red"))
    )
}

Generic spde2 model creation.

Description

Creates and inla.spde2 object describing the internal structure of an 'spde2' model.

Usage

inla.spde2.generic(
  M0,
  M1,
  M2,
  B0,
  B1,
  B2,
  theta.mu,
  theta.Q,
  transform = c("logit", "log", "identity"),
  theta.initial = theta.mu,
  fixed = rep(FALSE, length(theta.mu)),
  theta.fixed = theta.initial[fixed],
  BLC = cbind(0, diag(nrow = length(theta.mu))),
  ...
)

Arguments

M0

The symmetric M0 matrix.
M1

The square M1 matrix.
M2

The symmetric M2 matrix.
B0

Basis definition matrix for ϕ0\phi_0.
B1

Basis definition matrix for ϕ2\phi_2.
B2

Basis definition matrix for ϕ2\phi_2.
theta.mu

Prior expectation for the θ\theta vector
theta.Q

Prior precision for the θ\theta vector
transform

Transformation link for ϕ2\phi_2. Valid settings are "logit", "log", and "identity"
theta.initial

Initial value for the θ\theta vector. Default theta.mu
fixed

Logical vector. For every TRUE value, treat the corresponding theta value as known.
theta.fixed

Vector holding the values of fixed theta values. Default ⁠=theta.initial[fixed]⁠
BLC

Basis definition matrix for linear combinations of theta.
...

Additional parameters, currently unused.
theta

parameter values to be mapped.

Value

For inla.spde2.generic, an inla.spde2() object.

For inla.spde2.theta2phi0/1/2, a vector of ϕ\phi values.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde2.models(), inla.spde2.matern()

Matern SPDE model object for INLA

Description

Create an inla.spde2 model object for a Matern model. Use inla.spde2.pcmatern instead for a PC prior for the parameters.

Usage

inla.spde2.matern(
  mesh,
  alpha = 2,
  param = NULL,
  constr = FALSE,
  extraconstr.int = NULL,
  extraconstr = NULL,
  fractional.method = c("parsimonious", "null"),
  B.tau = matrix(c(0, 1, 0), 1, 3),
  B.kappa = matrix(c(0, 0, 1), 1, 3),
  prior.variance.nominal = 1,
  prior.range.nominal = NULL,
  prior.tau = NULL,
  prior.kappa = NULL,
  theta.prior.mean = NULL,
  theta.prior.prec = 0.1,
  n.iid.group = 1,
  ...
)

inla.spde2.theta2phi0(spde, theta)

inla.spde2.theta2phi1(spde, theta)

inla.spde2.theta2phi2(spde, theta)

Arguments

mesh

The mesh to build the model on, as an fmesher::fm_mesh_1d(), fmesher::fm_mesh_2d(), fmesher::fm_mesh_3d(), or fmesher::fm_collect() object.
alpha

Fractional operator order, 0<α≤20<\alpha\leq 2 supported. (ν=α−d/2\nu=\alpha-d/2)
param

Parameter, e.g. generated by param2.matern.orig
constr

If TRUE, apply an integrate-to-zero constraint. Default FALSE.
extraconstr.int

Field integral constraints.
extraconstr

Direct linear combination constraints on the basis weights.
fractional.method

Specifies the approximation method to use for fractional (non-integer) alpha values. 'parsimonious' gives an overall approximate minimal covariance error, 'null' uses approximates low-order properties.
B.tau

Matrix with specification of log-linear model for τ\tau.
B.kappa

Matrix with specification of log-linear model for κ\kappa.
prior.variance.nominal

Nominal prior mean for the field variance
prior.range.nominal

Nominal prior mean for the spatial range
prior.tau

Prior mean for tau (overrides prior.variance.nominal)
prior.kappa

Prior mean for kappa (overrides prior.range.nominal)
theta.prior.mean

(overrides ⁠prior.*⁠)
theta.prior.prec

Scalar, vector or matrix, specifying the joint prior precision for thetatheta.
n.iid.group

If greater than 1, build an explicitly iid replicated model, to support constraints applied to the combined replicates, for example in a time-replicated spatial model. Constraints can either be specified for a single mesh, in which case it's applied to the average of the replicates (ncol(A) should be mesh$n for 2D meshes, mesh$m for 1D), or as general constraints on the collection of replicates (ncol(A) should be mesh$n * n.iid.group for 2D meshes, mesh$m * n.iid.group for 1D).
...

Additional parameters for special uses.
spde

An spde model object
theta

Parameters in the model's internal scale

Details

This method constructs a Matern SPDE model, with spatial scale parameter κ(u)\kappa(u) and variance rescaling parameter τ(u)\tau(u).

(κ2(u)−Δ)α/2(τ(u)(\kappa^2(u)-\Delta)^{\alpha/2}(\tau(u)

x(u))=W(u)x(u))=W(u)

Stationary models are supported for 0<α≤20 < \alpha \leq 2, with spectral approximation methods used for non-integer α\alpha, with approximation method determined by fractional.method.

Non-stationary models are supported for α=2\alpha=2 only, with

  • log⁡τ(u)=B0τ(u)+∑k=1pBkτ(u)\log\tau(u) = B^\tau_0(u) + \sum_{k=1}^p B^\tau_k(u)θk\theta_k

  • log⁡κ(u)=B0κ(u)+∑k=1pBkκ(u)\log\kappa(u) = B^{\kappa}_0(u) + \sum_{k=1}^p B^{\kappa}_k(u)θk\theta_k

The same parameterisation is used in the stationary cases, but with B0τB^\tau_0, BkτB^\tau_k, B0κB^\kappa_0, and BkτB^\tau_k constant across uu.

Integration and other general linear constraints are supported via the constr, extraconstr.int, and extraconstr parameters, which also interact with n.iid.group.

Value

An inla.spde2 object.

Functions

  • inla.spde2.theta2phi0(): Convert from theta vector to phi0 values in the internal spde2 model representation

  • inla.spde2.theta2phi1(): Convert from theta vector to phi1 values in the internal spde2 model representation

  • inla.spde2.theta2phi2(): Convert from theta vector to phi2 values in the internal spde2 model representation

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

fmesher::fm_mesh_2d_inla(), fmesher::fm_rcdt_2d_inla(), fmesher::fm_mesh_1d(), fmesher::fm_basis(), inla.spde2.pcmatern(), inla.spde2.generic()

Examples

n <- 100
field.fcn <- function(loc) (10 * cos(2 * pi * 2 * (loc[, 1] + loc[, 2])))
loc <- matrix(runif(n * 2), n, 2)
## One field, 2 observations per location
idx.y <- rep(1:n, 2)
y <- field.fcn(loc[idx.y, ]) + rnorm(length(idx.y))

mesh <- fm_rcdt_2d_inla(loc, refine = list(max.edge = 0.05))
spde <- inla.spde2.matern(mesh)
data <- list(y = y, field = mesh$idx$loc[idx.y])
formula <- y ~ -1 + f(field, model = spde)
result <- inla(formula, data = data, family = "normal")

## Plot the mesh structure:
plot(mesh)

if (require(rgl)) {
    col.pal <- colorRampPalette(c("blue", "cyan", "green", "yellow", "red"))
    ## Plot the posterior mean:
    plot(mesh,
        rgl = TRUE,
        result$summary.random$field[, "mean"],
        color.palette = col.pal
    )
    ## Plot residual field:
    plot(mesh,
        rgl = TRUE,
        result$summary.random$field[, "mean"] - field.fcn(mesh$loc),
        color.palette = col.pal
    )
}

result.field <- inla.spde.result(result, "field", spde)
plot(result.field$marginals.range.nominal[[1]])

Approximate variance-compensating basis functions

Description

Calculates an approximate basis for tau and kappa for an inla.spde2.matern model where tau is a rescaling parameter.

Usage

inla.spde2.matern.sd.basis(
  mesh,
  B.sd,
  B.range,
  method = 1,
  local.offset.compensation = FALSE,
  alpha = 2,
  ...
)

Arguments

mesh

An fmesher::fm_mesh_1d(), fmesher::fm_mesh_2d(), or fmesher::fm_mesh_3d() object.
B.sd

Desired basis for log-standard deviations.
B.range

Desired basis for spatial range.
method

Construction method selector. Expert option only.
local.offset.compensation

If FALSE, only compensate in the average for the tau offset.
alpha

The model alpha parameter.
...

Additional parameters passed on to internal inla.spde2.matern calls.

Value

List of basis specifications

B.tau

Basis for log(tau)
B.kappa

Basis for log(kappa)

Intended for passing on to inla.spde2.matern().

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde2.matern()

Matern SPDE model object with PC prior for INLA

Description

Create an inla.spde2 model object for a Matern model, using a PC prior for the parameters.

Usage

inla.spde2.pcmatern(
  mesh,
  alpha = 2,
  param = NULL,
  constr = FALSE,
  extraconstr.int = NULL,
  extraconstr = NULL,
  fractional.method = c("parsimonious", "null"),
  n.iid.group = 1,
  prior.range = NULL,
  prior.sigma = NULL
)

Arguments

mesh

The mesh to build the model on, as an fmesher::fm_mesh_2d() object, or other fmesher mesh supporting fmesher::fm_dof(), fmesher::fm_manifold_dim() and fmesher::fm_dof(), such as fmesher::fm_mesh_1d(), fmesher::fm_mesh_3d(), or fmesher::fm_collect().
alpha

Fractional operator order, 0<α≤20<\alpha\leq 2 supported, for ν=α−d/2>0\nu=\alpha-d/2>0.
param

Further model parameters. Not currently used.
constr

If TRUE, apply an integrate-to-zero constraint. Default FALSE.
extraconstr.int

Field integral constraints.
extraconstr

Direct linear combination constraints on the basis weights.
fractional.method

Specifies the approximation method to use for fractional (non-integer) alpha values. 'parsimonious' gives an overall approximate minimal covariance error, 'null' uses approximates low-order properties.
n.iid.group

If greater than 1, build an explicitly iid replicated model, to support constraints applied to the combined replicates, for example in a time-replicated spatial model. Constraints can either be specified for a single mesh, in which case it's applied to the average of the replicates (ncol(A) should be mesh$n for 2D meshes, mesh$m for 1D), or as general constraints on the collection of replicates (ncol(A) should be mesh$n * n.iid.group for 2D meshes, mesh$m * n.iid.group for 1D).
prior.range

A length 2 vector, with ⁠(range0,Prange)⁠ specifying that P(ρ<ρ0)=pρP(\rho < \rho_0)=p_\rho, where ρ\rho is the spatial range of the random field. If Prange is NA, then range0 is used as a fixed range value.
prior.sigma

A length 2 vector, with ⁠(sigma0,Psigma)⁠ specifying that P(σ>σ0)=pσP(\sigma > \sigma_0)=p_\sigma, where σ\sigma is the marginal standard deviation of the field. If Psigma is NA, then sigma0 is used as a fixed range value.

Details

This method constructs a Matern SPDE model, with spatial range ρ\rho and standard deviation parameter σ\sigma. In the parameterisation

(κ2−Δ)α/2(τ(\kappa^2-\Delta)^{\alpha/2}(\tau

x(u))=W(u)x(u))=W(u)

the spatial scale parameter κ=8ν/ρ\kappa=\sqrt{8\nu}/\rho, where ν=α−d/2\nu=\alpha-d/2, and τ\tau is proportional to 1/σ1/\sigma.

Stationary models are supported for 0<α≤20 < \alpha \leq 2, with spectral approximation methods used for non-integer α\alpha, with approximation method determined by fractional.method.

Integration and other general linear constraints are supported via the constr, extraconstr.int, and extraconstr parameters, which also interact with n.iid.group.

The joint PC prior density for the spatial range, ρ\rho, and the marginal standard deviation, σ\sigma, and is

π(ρ,σ)=\pi(\rho, \sigma) =

dλρ2ρ−1−d/2exp⁡(−λρρ−d/2)\frac{d \lambda_\rho}{2} \rho^{-1-d/2} \exp(-\lambda_\rho \rho^{-d/2})

λσexp⁡(−λσσ)\lambda_\sigma\exp(-\lambda_\sigma \sigma)

where λρ\lambda_\rho and λσ\lambda_\sigma are hyperparameters that must be determined by the analyst. The practical approach for this in INLA is to require the user to indirectly specify these hyperparameters through

P(ρ<ρ0)=pρP(\rho < \rho_0) = p_\rho

and

P(σ>σ0)=pσP(\sigma > \sigma_0) = p_\sigma

where the user specifies the lower tail quantile and probability for the range (ρ0\rho_0 and pρp_\rho) and the upper tail quantile and probability for the standard deviation (σ0\sigma_0 and ασ\alpha_\sigma).

This allows the user to control the priors of the parameters by supplying knowledge of the scale of the problem. What is a reasonable upper magnitude for the spatial effect and what is a reasonable lower scale at which the spatial effect can operate? The shape of the prior was derived through a construction that shrinks the spatial effect towards a base model of no spatial effect in the sense of distance measured by Kullback-Leibler divergence.

The prior is constructed in two steps, under the idea that having a spatial field is an extension of not having a spatial field. First, a spatially constant random effect (ρ=∞\rho = \infty) with finite variance is more complex than not having a random effect (σ=0\sigma = 0). Second, a spatial field with spatial variation (ρ<∞\rho < \infty) is more complex than the random effect with no spatial variation. Each of these extensions are shrunk towards the simpler model and, as a result, we shrink the spatial field towards the base model of no spatial variation and zero variance (ρ=∞\rho = \infty and σ=0\sigma = 0).

The details behind the construction of the prior is presented in Fuglstad, et al. (2016) and is based on the PC prior framework (Simpson, et al., 2015).

Value

An inla.spde2 object.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

References

Fuglstad, G.-A., Simpson, D., Lindgren, F., and Rue, H. (2016) Constructing Priors that Penalize the Complexity of Gaussian Random Fields. arXiv:1503.00256

Simpson, D., Rue, H., Martins, T., Riebler, A., and Sørbye, S. (2015) Penalising model component complexity: A principled, practical approach to constructing priors. arXiv:1403.4630

See Also

fmesher::fm_mesh_2d_inla(), fmesher::fm_rcdt_2d_inla(), fmesher::fm_mesh_1d(), fmesher::fm_basis(), inla.spde2.matern(), inla.spde2.generic()

Examples

## Spatial interpolation
n <- 100
field.fcn <- function(loc) (10 * cos(2 * pi * 2 * (loc[, 1] + loc[, 2])))
loc <- matrix(runif(n * 2), n, 2)
## One field, 2 observations per location
idx.y <- rep(1:n, 2)
y <- field.fcn(loc[idx.y, ]) + rnorm(length(idx.y))

mesh <- fm_mesh_2d_inla(loc, max.edge = 0.05, cutoff = 0.01)
spde <- inla.spde2.pcmatern(mesh,
    prior.range = c(0.01, 0.1), prior.sigma = c(100, 0.1)
)
data <- list(y = y, field = mesh$idx$loc[idx.y])
formula <- y ~ -1 + f(field, model = spde)
result <- inla(formula, data = data, family = "normal")

## Plot the mesh structure:
plot(mesh)

if (require(rgl)) {
    col.pal <- colorRampPalette(c("blue", "cyan", "green", "yellow", "red"))
    ## Plot the posterior mean:
    plot(mesh,
        rgl = TRUE,
        result$summary.random$field[, "mean"],
        color.palette = col.pal
    )
    ## Plot residual field:
    plot(mesh,
        rgl = TRUE,
        result$summary.random$field[, "mean"] - field.fcn(mesh$loc),
        color.palette = col.pal
    )
}


result.field <- inla.spde.result(result, "field", spde)
par(mfrow = c(2, 1))
plot(result.field$marginals.range.nominal[[1]],
    type = "l", main = "Posterior density for range"
)
plot(inla.tmarginal(sqrt, result.field$marginals.variance.nominal[[1]]),
    type = "l", main = "Posterior density for std.dev."
)
par(mfrow = c(1, 1))

## Spatial model
set.seed(1234234)

## Generate spatial locations
nObs <- 200
loc <- matrix(runif(nObs * 2), nrow = nObs, ncol = 2)

## Generate observation of spatial field
nu <- 1.0
rhoT <- 0.2
kappaT <- sqrt(8 * nu) / rhoT
sigT <- 1.0
Sig <- sigT^2 * inla.matern.cov(
    nu = nu,
    kappa = kappaT,
    x = as.matrix(dist(loc)),
    d = 2,
    corr = TRUE
)
L <- t(chol(Sig))
u <- L %*% rnorm(nObs)

## Construct observation with nugget
sigN <- 0.1
y <- u + sigN * rnorm(nObs)

## Create the mesh and spde object
mesh <- fm_mesh_2d_inla(loc,
    max.edge = 0.05,
    cutoff = 0.01
)
spde <- inla.spde2.pcmatern(mesh,
    prior.range = c(0.01, 0.05),
    prior.sigma = c(10, 0.05)
)

## Create projection matrix for observations
A <- fm_basis(mesh = mesh, loc = loc)

## Run model without any covariates
idx <- 1:spde$n.spde
res <- inla(y ~ f(idx, model = spde) - 1,
    data = list(y = y, idx = idx, spde = spde),
    control.predictor = list(A = A)
)

## Re-run model with fixed range
spde.fixed <- inla.spde2.pcmatern(mesh,
    prior.range = c(0.2, NA),
    prior.sigma = c(10, 0.05)
)

res.fixed <- inla(y ~ f(idx, model = spde) - 1,
    data = list(y = y, idx = idx, spde = spde.fixed),
    control.predictor = list(A = A)
)

Wrapper method for fmesher::fm_transform

Description

[Deprecated] in favour of fmesher::fm_transform().

Handles transformation of various inla objects according to coordinate reference systems of sf::crs, sp::CRS or inla.CRS class.

Usage

inla.spTransform(x, CRSobj, ...)

Arguments

x

The object that should be transformed from it's current CRS to a new CRS
CRSobj

passed on as the crs argument to fmesher::fm_transform().
...

Potential other arguments for fmesher::fm_transform().

Value

The object is returned with its coordinates transformed

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.CRS()

Examples

if (require("sf") && require("sp") && require("fmesher")) {
    latt <- inla.mesh.lattice(-10:10, 40:60)
    mesh1 <- inla.mesh.create(
        lattice = latt, extend = FALSE, refine = FALSE,
        crs = fm_CRS("longlat_norm")
    )
    mesh2 <- fm_transform(mesh1, fm_crs("lambert_globe"))
    print(summary(mesh1))
    print(summary(mesh2))
}

Setup remote computing

Description

Initialize the definition file and print the path to the internal script to transfer ssh-keys

Usage

inla.ssh.copy.id()

inla.remote()

Value

inla.remote is used once to setup the remote host information file (definition file) in the users home directory; see the FAQ entry on this issue for more information. inla.ssh.copy.id will return the path to the internal script to transfer ssh-keys.

Author(s)

Havard Rue hrue@r-inla.org

Examples

##See the FAQ entry on this issue on r-inla.org.

Data stacking for advanced INLA models

Description

Functions for combining data, effects and observation matrices into inla.stack objects, and extracting information from such objects.

Usage

inla.stack.remove.unused(stack)

inla.stack.compress(stack, remove.unused = TRUE)

inla.stack(..., compress = TRUE, remove.unused = TRUE, multi.family = FALSE)

inla.stack.sum(
  data,
  A,
  effects,
  responses = NULL,
  tag = "",
  compress = TRUE,
  remove.unused = TRUE
)

inla.stack.join(
  ...,
  compress = TRUE,
  remove.unused = TRUE,
  multi.family = FALSE
)

inla.stack.index(stack, tag)

inla.stack.LHS(stack)

inla.stack.RHS(stack)

inla.stack.data(stack, ..., .response.name = NULL)

inla.stack.A(stack)

inla.stack.response(stack, drop = TRUE)

## S3 method for class 'inla.data.stack'
print(x, ...)

Arguments

stack

A inla.data.stack object, created by a call to inla.stack, inla.stack.sum, or inla.stack.join.
remove.unused

If TRUE, compress the model by removing rows of effects corresponding to all-zero columns in the A matrix (and removing those columns).
...

For inla.stack.join, two or more data stacks of class inla.data.stack, created by a call to inla.stack, inla.stack.sum, or inla.stack.join. For inla.stack.data, a list of variables to be joined with the data list.
compress

If TRUE, compress the model by removing duplicated rows of effects, replacing the corresponding A-matrix columns with a single column containing the sum.
multi.family

logical or character. For inla.data.join, if TRUE, the response part of the stack is joined as a list. If character, denotes the name of a data element that should be joined as a multi-column matrix. Default is FALSE, which joins both the data and responses elements with regular row binding with dplyr::bind_rows.
data

A list or codedata.frame of named data vectors. Scalars are expanded to match the number of rows in the A matrices, or any non-scalar data vectors. An error is given if the input is inconsistent.
A

A list of observation matrices. Scalars are expanded to diagonal matrices matching the effect vector lengths. An error is given if the input is inconsistent or ambiguous.
effects

A collection of effects/predictors. Each list element corresponds to an observation matrix, and must either be a single vector, a list of vectors, or a data.frame. Single-element effect vectors are expanded to vectors matching the number of columns in the corresponding A matrix. An error is given if the input is inconsistent or ombiguous.
responses

A list of response vectors, matrices, data.frame, or other special response objects, such as inla.mdata() and inla.surv(). Each list element corresponds to one response family. In ordinary user-side code, the list has length 1, and longer lists are created by joining stacks with inla.stack(..., multi.family = TRUE).
tag

A string specifying a tag for later identification.
.response.name

The name to assign to the response variable when extracting data from the stack. Default is NULL, which skips the response object list.
drop

logical indicating whether to return the contained object instead of the full list, when the stack responses list has length 1. Default is TRUE, as needed for inla() single family models. Use drop = FALSE to extract the internal response storage, regardless of length.
x

An inla.data.stack object for printing

Details

For models with a single effects collection, the outer list container for A and effects may be omitted.

Component size definitions:

  • nln_l effect blocks

  • nkn_k effects

  • nin_i data values

  • nj,ln_{j,l} effect size for block ll

  • njn_j =∑l=1nlnj,l= \sum_{l=1}^{n_l} n_{j,l} total effect size

Input:

data

(y1,…,yp)(y^1, \ldots, y^p) pp vectors, each of length nin_i

A

(A1,…,Anl)(A^1, \ldots, A^{n_l}) matrices of size ni×nj,ln_i \times n_{j,l}

effects

((x1,1,…,xnk,1),…,(x1,nl,…,xnk,nl))\left((x^{1,1},\ldots,x^{n_k,1}), \ldots, (x^{1,n_l},\ldots,x^{n_k,n_l})\right) collections of effect vectors of length nj,ln_{j,l}

predictor(y1,…,yp)∼∑l=1nlAl∑k=1nkg(k,xk,l)=A~∑k=1nkg(k,x~k)\mbox{predictor}(y^1, \ldots, y^p) \sim \sum_{l=1}^{n_l} A^l \sum_{k=1}^{n_k} g(k, x^{k,l}) = \tilde{A} \sum_{k=1}^{n_k} g(k, \tilde{x}^k)

where

A~=cbind(A1,…,Anl)\tilde{A} = \mbox{cbind}\left( A^1, \ldots, A^{n_l} \right)

and

x~k=rbind(xk,1,…,xk,nl)\tilde{x}^k = \mbox{rbind}\left( x^{k,1}, \ldots, x^{k,n_l} \right)

and for each block ll, any missing xk,lx^{k,l} is replaced by an NA vector.

Value

A data stack of class inla.data.stack. Elements:

  • data =(y1,…,yp,x~1,…,x~nk)=(y^1, \ldots, y^p, \tilde{x}^1, \ldots, \tilde{x}^{n_k})

  • A =A~=\tilde{A}

  • data.names List of data names, length pp

  • effect.names List of effect names, length nkn_k

  • n.data Data length, nin_i

  • index List indexed by tags, each element indexing into i=1,…,nii=1, \ldots, n_i

Functions

  • inla.stack.remove.unused(): Remove unused entries from an existing stack

  • inla.stack.compress(): Compress an existing stack by removing duplicates

  • inla.stack.sum(): Create data stack as a sum of predictors

  • inla.stack.join(): Join two or more data stacks

  • inla.stack.index(): Extract tagged indices

  • inla.stack.LHS(): Extract data associated with the "left hand side" of the model (e.g. the data itself, Ntrials, link, E)

  • inla.stack.RHS(): Extract data associated with the "right hand side" of the model (all the covariates/predictors)

  • inla.stack.data(): Extract data for an inla call, and optionally join with other variables

  • inla.stack.A(): Extract the "A matrix" for control.predictor

  • inla.stack.response(): Extract the response variable or list of response objects

  • print(inla.data.stack): Print information about an inla.data.stack

Functions

  • inla.stack.remove.unused: Remove unused entries from an existing stack

  • inla.stack.compress: Compress an existing stack by removing duplicates

  • inla.stack: Shorthand for inla.stack.join and inla.stack.sum

  • inla.stack.sum: Create data stack as a sum of predictors

  • inla.stack.join: Join two or more data stacks

  • inla.stack.index: Extract tagged indices

  • inla.stack.LHS: Extract data associated with the "left hand side" of the model (e.g. the data itself, Ntrials, link, E)

  • inla.stack.RHS: Extract data associated with the "right hand side" of the model (all the covariates/predictors)

  • inla.stack.data: Extract data for an inla call, and optionally join with other variables

  • inla.stack.A: Extract the "A matrix" for control.predictor

See Also

inla.spde.make.A(), inla.spde.make.index()

Examples

library(fmesher)
n <- 200
loc <- matrix(runif(n * 2), n, 2)
mesh <- fm_mesh_2d(
    loc.domain = loc,
    max.edge = c(0.05, 0.2)
)
proj.obs <- fm_evaluator(mesh, loc = loc)
proj.pred <- fm_evaluator(mesh, loc = mesh$loc)
spde <- inla.spde2.pcmatern(mesh,
    prior.range = c(0.01, 0.01),
    prior.sigma = c(10, 0.01)
)

covar <- rnorm(n)
field <- inla.qsample(n = 1, Q = inla.spde.precision(spde, theta = log(c(0.5, 1))))[, 1]
y <- 2 * covar + fm_evaluate(proj.obs, field)

A.obs <- inla.spde.make.A(mesh, loc = loc)
A.pred <- inla.spde.make.A(mesh, loc = proj.pred$loc)
stack.obs <-
    inla.stack(
        data = list(y = y),
        A = list(A.obs, 1),
        effects = list(c(
            list(Intercept = 1),
            inla.spde.make.index("spatial", spde$n.spde)
        ),
        covar = covar
        ),
        tag = "obs"
    )
stack.pred <-
    inla.stack(
        data = list(y = NA),
        A = list(A.pred),
        effects = list(c(
            list(Intercept = 1),
            inla.spde.make.index("spatial", mesh$n)
        )),
        tag = "pred"
    )
stack <- inla.stack(stack.obs, stack.pred)

formula <- y ~ -1 + Intercept + covar + f(spatial, model = spde)
result1 <- inla(formula,
    data = inla.stack.data(stack.obs, spde = spde),
    family = "gaussian",
    control.predictor = list(
        A = inla.stack.A(stack.obs),
        compute = TRUE
    )
)

plot(y, result1$summary.fitted.values[inla.stack.index(stack.obs, "obs")$data, "mean"],
    main = "Observations vs posterior predicted values at the data locations"
)

result2 <- inla(formula,
    data = inla.stack.data(stack, spde = spde),
    family = "gaussian",
    control.predictor = list(
        A = inla.stack.A(stack),
        compute = TRUE
    )
)

field.pred <- fm_evaluate(
    proj.pred,
    result2$summary.fitted.values[inla.stack.index(stack, "pred")$data, "mean"]
)
field.pred.sd <- fm_evaluate(
    proj.pred,
    result2$summary.fitted.values[inla.stack.index(stack, "pred")$data, "sd"]
)

plot(field, field.pred, main = "True vs predicted field")
abline(0, 1)
image(fm_evaluate(mesh,
    field = field,
    dims = c(200, 200)
),
main = "True field"
)
image(fm_evaluate(mesh,
    field = field.pred,
    dims = c(200, 200)
),
main = "Posterior field mean"
)
image(fm_evaluate(mesh,
    field = field.pred.sd,
    dims = c(200, 200)
),
main = "Prediction standard deviation"
)
plot(field, (field.pred - field) / 1,
    main = "True field vs standardised prediction residuals"
)

Create a Survival Object for INLA

Description

Create a survival object, to be used as a response variable in a model formula for the inla() function for survival models.

Usage

inla.surv(time, event, time2, truncation, subject = NULL, cure = NULL)

## S3 method for class 'inla.surv'
plot(x, y, ...)

## S3 method for class 'inla.surv'
print(x, ...)

as.inla.surv(object, ...)

is.inla.surv(object)

Arguments

time

For right censored data, this is the follow up time. For interval data, this is the starting time for the interval. For in-interval event, this is the observed time (in the interval) for the event. For left censored data, this the censoring time.
event

The status indicator, 1=observed event, 0=right censored event, 2=left censored event, 3=interval censored event, and 4=observed event in an interval (left, right).
time2

Ending time for the interval censored data or an in-interval event.
truncation

Left truncation. If missing it is assumed to be 0. The lower limit for event=4.
subject

Patient number in multiple event data, not needed otherwise.
cure

A matrix of covariates that can be used with a cure-model.
x

Object to plot or print
y

Object to plot (not in use)
...

Additional argument
object

Any R-object

Value

An object of class inla.surv. There are methods for print, plot for inla.surv objects.

is.inla.surv returns TRUE if object inherits from class inla.surv, otherwise FALSE.

as.inla.surv returns an object of class inla.surv

Author(s)

Sara Martino, Rupali Akerkar and Haavard Rue

See Also

inla()

Examples

## First example
  trt = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
          0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
          1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
  time = c(17,42,44,48,60,72,74,95,103, 108, 122, 144, 167, 170, 183, 185,
           193, 195, 197, 208, 234, 235, 254, 307, 315, 401, 445, 464, 484,  528, 542, 567,
           577, 580, 795, 855, 1174, 1214, 1232, 1366, 1455, 1585, 1622, 1626, 1736, 1,63,
           105, 125, 182, 216, 250, 262, 301, 301, 342, 354, 356, 358, 380, 383, 383, 388,
           394, 408, 460, 489, 499, 523, 524, 535, 562, 569, 675, 676, 748, 778, 786, 797,
           955, 968, 977, 1245, 1271, 1420, 1460, 1516, 1551, 1690, 1694)
  event = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
            1,1,1,1,0,1,0,1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
            1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,0,1)
  y = inla.surv(time, event)

  ## Second example
  time = c(182,182,63,68,182,152,182,130,134,145,152,182,98,152,182,88,95,105,130,137,167,182,
           152,182,81,182,71,84,126,134,152,182)
  event = c(1,0,1,1,0,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,1,1,0)
  subject = c(1,2,3,3,3,4,4,5,5,5,5,5,6,6,6,7,7,7,7,7,7,7,8,8,9,9,10,10,10,10,10,10)
  y = inla.surv(time, event, subject=subject)

Upgrade the INLA-package

Description

Functions to upgrade the INLA-package to the current version.

Usage

inla.update(lib = NULL, testing = FALSE, ask = TRUE)

inla.upgrade(lib = NULL, testing = FALSE, ask = TRUE)

Arguments

lib

Location to install the library.
testing

If TRUE, then look for a test-version if the INLA-package.
ask

same argument as in update.packages

Value

inla.upgrade will update the INLA package to the current version, and inla.update do the same for backward compatibility. This function is simple wrapper for update.packages using the INLA repository.

Author(s)

Havard Rue hrue@r-inla.org

See Also

update.packages

Show the version of the INLA-package

Description

Show the version of the INLA-package

Usage

inla.version(what = c("default", "version", "date"))

Arguments

what

What to show version of

Value

inla.version display the current version information using cat with default or info, or return other specific requests through the call.

Author(s)

Havard Rue hrue@r-inla.org

Examples

## Summary of all
inla.version()
## The building date
inla.version("date")

Sample, transform and evaluate from a joint marginal approximation

Description

Sample, transform and evaluate from from a joint marginal approximation as returned using argument selection in inla.

Usage

inla.rjmarginal(n, jmarginal, constr)

inla.rjmarginal.eval(fun, samples, ...)

## S3 method for class 'inla.jmarginal'
print(x, ...)

## S3 method for class 'inla.jmarginal'
summary(object, ...)

## S3 method for class 'summary.inla.jmarginal'
print(x, ...)

inla.tjmarginal(jmarginal, A)

inla.1djmarginal(jmarginal)

Arguments

n

The number of samples
jmarginal

A marginal object given either by a inla object or result$selection
constr

Optional linear constraints; see ?INLA::f and argument extraconstr
fun

A function which is evaluated for each sample, similar to inla.posterior.sample.eval: please see the documentation for this functions for details.
samples

The samples, as in the form of the output from inla.rjmarginal
...

Arguments passed on to other methods (printing and summarising)
x

Object to be printed
object

Object to be summarised
A

A matrix used for the linear combination

Value

THESE FUNCTIONS ARE EXPERIMENTAL FOR THE MOMENT (JULY 2020)

inla.rjmarginal returns a list with the samples in samples (matrix) and the corresponding log-densities in log.density (vector). Each column in samples contains one sample.

inla.rjmarginal.eval returns a matrix, where each row is the (vector) function evaluated at each sample.

inla.tjmarginal returns a inla.jmarginal-object of the linear combination defined by the matrix A.

inla.1djmarginal return the marginal densities from a joint approximation.

Author(s)

Cristian Chiuchiolo and Havard Rue hrue@r-inla.org

See Also

inla()

Examples

n = 10
 x = 1+rnorm(n)
 xx = 3 + rnorm(n)
 y = 1 + x + xx + rnorm(n)
 selection = list(xx=1, x=1)
 r = inla(y ~ 1 + x + xx,
          data = data.frame(y, x, xx),
          selection = selection)
 ns = 100
 xx = inla.rjmarginal(ns, r)

 print(cbind(mean = r$selection$mean, sample.mean = rowMeans(xx$samples)))
 print("cov matrix")
 print(round(r$selection$cov.matrix, dig=3))
 print("sample cov matrix")
 print(round(cov(t(xx$samples)), dig=3))

 skew = function(z) mean((z-mean(z))^3)/var(z)^1.5
 print(round(cbind(skew = r$selection$skewness,
                   sample.skew = apply(xx$samples, 1, skew)), digits = 3))

 ## illustrating the eval function
 n = 10
 x = rnorm(n)
 eta = 1 + x
 y = eta + rnorm(n, sd=0.1)
 selection = list(x = 1, '(Intercept)' = 1)
 r = inla(y ~ 1 + x,
          data = data.frame(y, x),
          selection = selection)
 xx = inla.rjmarginal(100,  r)
 xx.eval = inla.rjmarginal.eval(function() c(x, Intercept),  xx)
 print(cbind(xx$samples[, 1]))
 print(cbind(xx.eval[, 1]))

 constr <- list(A = matrix(1, ncol = nrow(xx$samples), nrow = 1), e = 1)
 x <- inla.rjmarginal(10, r, constr = constr)

 A <- matrix(rnorm(nrow(xx$samples)^2), nrow(xx$samples), nrow(xx$samples))
 b <- inla.tjmarginal(r, A)
 b.marg <- inla.1djmarginal(b)

Joint-prior models

Description

A framework for defining joint priors in R

Usage

inla.jp.define(jp = NULL, ...)

Arguments

jp

The jp-function which returns the joint log-prior as a function of argument theta. There is an optional second argument that is a vector of theta-names. If second argument is not present, argument .theta.desc will be added.
...

Named list of variables that defines the environment of jp

Value

This allows joint priors to be defined in R.

This function is for internal use only.

Author(s)

Havard Rue hrue@r-inla.org

Kidney infection data

Description

Times of infection from the time to insertion of the catheter for 38 kindey patients using portable dialysis equipment

Format

A data frame with 76 observations on the following 9 variables.

time

a numeric vector. Time to infection from the insertion of catheter

event

a numeric vector. 1: time of infection 0: time of censuring

age

a numeric vector. Age of the patient at the time of infection

sex

a numeric vector. Sex of the patient 0: male 1:female

disease

a numeric vector. Type of disease

dis1

a numeric vector. Dummy variable to codify the disease type.

dis2

a numeric vector. Dummy variable to codify the disease type.

dis3

a numeric vector. Dummy variable to codify the disease type.

ID

a numeric vector. Patient code.

References

McGilchrist and C.W. Aisbett (1991), Regression with frailty in survival analysis, Biometrics,vol.47,pages 461–166.

D.J. Spiegelhalter and A. Thomas and N.G. Best and W.R. Gilks (1995) BUGS: Bayesian Inference Using Gibbs sampling, Version 0.50., MRC Biostatistics Unit, Cambridre, England.

Functions to define mapping between a lattice and nodes

Description

These functions define mapping in between two-dimensional indices on a lattice and the one-dimensional node representation used in inla.

Usage

inla.lattice2node.mapping(nrow, ncol)

inla.node2lattice.mapping(nrow, ncol)

inla.lattice2node(irow, icol, nrow, ncol)

inla.node2lattice(node, nrow, ncol)

inla.matrix2vector(a.matrix)

inla.vector2matrix(a.vector, nrow, ncol)

Arguments

nrow

Number of rows in the lattice.
ncol

Number of columns in the lattice.
irow

Lattice row index, between 1 and nrow
icol

Lattice column index, between 1 and ncol
node

The node index, between 1 and ncol*nrow
a.matrix

is a matrix to be mapped to a vector using internal representation defined by inla.lattice2node
a.vector

is a vector to be mapped into a matrix using the internal representation defined by inla.node2lattice

Details

The mapping from node to lattice follows the default R behaviour (which is column based storage), and as.vector(A) and matrix(a, nrow, ncol) can be used instead of inla.matrix2vector and inla.vector2matrix.

Value

inla.lattice2node.mapping returns the hole mapping as a matrix, and inla.node2lattice.mapping returns the hole mapping as list(irow=..., icol=...). inla.lattice2node and inla.node2lattice provide the mapping for a given set of lattice indices and nodes. inla.matrix2vector provide the mapped vector from a matrix, and inla.vector2matrix provide the inverse mapped matrix from vector.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla

Examples

## write out the mapping using the two alternatives
nrow = 2
ncol = 3
mapping = inla.lattice2node.mapping(nrow,ncol)

for (i in 1:nrow){
    for(j in 1:ncol){
        print(paste("Alt.1: lattice index [", i,",", j,"] corresponds",
                    "to node [", mapping[i,j],"]", sep=""))
    }
}

for (i in 1:nrow){
    for(j in 1:ncol){
        print(paste("Alt.2: lattice index [", i,",", j,"] corresponds to node [",
                    inla.lattice2node(i,j,nrow,ncol), "]", sep=""))
    }
}

inv.mapping = inla.node2lattice.mapping(nrow,ncol)
for(node in 1:(nrow*ncol))
   print(paste("Alt.1: node [", node, "] corresponds to lattice index [",
               inv.mapping$irow[node], ",",
               inv.mapping$icol[node],"]", sep=""))

for(node in 1:(nrow*ncol))
   print(paste("Alt.2: node [", node, "] corresponds to lattice index [",
               inla.node2lattice(node,nrow,ncol)$irow[1], ",",
               inla.node2lattice(node,nrow,ncol)$icol[1],"]", sep=""))

## apply the mapping from matrix to vector and back
n = nrow*ncol
z = matrix(1:n,nrow,ncol)
z.vector = inla.matrix2vector(z)  # as.vector(z) could also be used
print(mapping)
print(z)
print(z.vector)

## the vector2matrix is the inverse, and should give us the z-matrix
## back. matrix(z.vector, nrow, ncol) could also be used here.
z.matrix = inla.vector2matrix(z.vector, nrow, ncol)
print(z.matrix)

The Leukemia data

Description

This the Leukemia data from Henderson et al (2003); see source.

Format

A data frame with 1043 observations on the following 9 variables.

time

TODO

cens

TODO

xcoord

TODO

ycoord

TODO

age

TODO

sex

TODO

wbc

TODO

tpi

TODO

district

TODO

Source

This is the dataset from

Henderson, R. and Shimakura, S. and Gorst, D., 2002, Modeling spatial variation in leukemia survival data, JASA, 97, 460, 965–972.

Examples

data(Leuk)

Draw inla.mesh.segment objects.

Description

[Deprecated] Use fmesher::lines.fm_segm() or fmesher::lines_rgl() instead.

Draws a inla.mesh.segment() object with generic or rgl graphics.

Usage

## S3 method for class 'inla.mesh.segment'
lines(
  x,
  loc = NULL,
  col = NULL,
  colors = c("black", "blue", "red", "green"),
  add = TRUE,
  xlim = NULL,
  ylim = NULL,
  rgl = FALSE,
  ...
)

Arguments

x

An inla.mesh.segment() object.
loc

Point locations to be used if x$loc is NULL.
col

Segment color specification.
colors

Colors to cycle through if col is NULL.
add

If TRUE, add to the current plot, otherwise start a new plot.
xlim

X axis limits for a new plot.
ylim

Y axis limits for a new plot.
rgl

If TRUE, use rgl for plotting.
...

Additional parameters, passed on to graphics methods.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.segment()

Link functions in INLA

Description

Define link-functions and its inverse

Usage

inla.link.cauchit(x, inverse = FALSE)

inla.link.invcauchit(x, inverse = FALSE)

inla.link.log(x, inverse = FALSE)

inla.link.invlog(x, inverse = FALSE)

inla.link.neglog(x, inverse = FALSE)

inla.link.invneglog(x, inverse = FALSE)

inla.link.logit(x, inverse = FALSE)

inla.link.invlogit(x, inverse = FALSE)

inla.link.probit(x, inverse = FALSE)

inla.link.invprobit(x, inverse = FALSE)

inla.link.robit(x, df = 7, inverse = FALSE)

inla.link.invrobit(x, df = 7, inverse = FALSE)

inla.link.loglog(x, inverse = FALSE)

inla.link.invloglog(x, inverse = FALSE)

inla.link.cloglog(x, inverse = FALSE)

inla.link.invcloglog(x, inverse = FALSE)

inla.link.ccloglog(x, inverse = FALSE)

inla.link.invccloglog(x, inverse = FALSE)

inla.link.tan(x, inverse = FALSE)

inla.link.invtan(x, inverse = FALSE)

inla.link.tan.pi(x, inverse = FALSE)

inla.link.invtan.pi(x, inverse = FALSE)

inla.link.identity(x, inverse = FALSE)

inla.link.invidentity(x, inverse = FALSE)

inla.link.inverse(x, inverse = FALSE)

inla.link.invinverse(x, inverse = FALSE)

inla.link.invqpoisson(x, inverse = FALSE, quantile = 0.5)

inla.link.sn(x, intercept = 0.5, skew = 0, a = NULL, inverse = FALSE)

inla.link.invsn(x, intercept = 0.5, skew = 0, a = NULL, inverse = FALSE)

inla.link.gevit(x, tail = 0.1, inverse = FALSE)

inla.link.invgevit(x, tail = 0.1, inverse = FALSE)

inla.link.cgevit(x, tail = 0.1, inverse = FALSE)

inla.link.invcgevit(x, tail = 0.1, inverse = FALSE)

inla.link.invalid(x, inverse = FALSE)

inla.link.invinvalid(x, inverse = FALSE)

Arguments

x

The argument. A numeric vector.
inverse

Logical. Use the link (inverse=FALSE) or its inverse (inverse=TRUE)
df

The degrees of freedom for the Student-t
quantile

The quantile level for quantile links
intercept

The quantile level for the intercept in the Skew-Normal link
skew

The skewness in the Skew-Normal. Only one of skew and a can be given.
a

The a-parameter in the Skew-Normal. Only one of skew and a can be given.
tail

The tail parameter in the GEV distribution (0 < tail <= 1/2)

Value

Return the values of the link-function or its inverse.

Note

The inv-functions are redundant, as inla.link.invlog(x) = inla.link.log(x, inverse=TRUE) and so on, but they are simpler to use as arguments to other functions.

Author(s)

Havard Rue hrue@r-inla.org

Create linear combinations

Description

Create a linear combination or several linear combinations, as input to ⁠inla(..., lincomb = <lincomb>)⁠

Usage

inla.make.lincomb(...)

inla.make.lincombs(...)

Arguments

...

Arguments; see examples

Value

A structure to be passed on to inla() argument lincomb

Author(s)

Havard Rue hrue@r-inla.org

See Also

TODO

Examples

##See the worked out examples and description in the OLD-FAQ
##vignette {vignette("old-faq", package="INLA")}

Functions which operates on marginals

Description

Density, distribution function, quantile function, random generation, hpd-interval, interpolation, expectations, mode and transformations of marginals obtained by inla or inla.hyperpar(). These functions computes the density (inla.dmarginal), the distribution function (inla.pmarginal), the quantile function (inla.qmarginal), random generation (inla.rmarginal), spline smoothing (inla.smarginal), computes expected values (inla.emarginal), computes the mode (inla.mmarginal), transforms the marginal (inla.tmarginal), and provide summary statistics (inla.zmarginal).

Usage

inla.smarginal(
  marginal,
  log = FALSE,
  extrapolate = 0,
  keep.type = FALSE,
  factor = 15L
)

inla.emarginal(fun, marginal, ...)

inla.dmarginal(x, marginal, log = FALSE)

inla.pmarginal(q, marginal, normalize = TRUE, len = 2048L)

inla.qmarginal(p, marginal, len = 2048L)

inla.hpdmarginal(p, marginal, len = 2048L)

inla.rmarginal(n, marginal)

inla.tmarginal(
  fun,
  marginal,
  n = 2048L,
  h.diff = .Machine[["double.eps"]]^(1/3),
  method = c("quantile", "linear")
)

inla.mmarginal(marginal)

inla.zmarginal(marginal, silent = FALSE)

inla.is.marginal(marginal)

Arguments

marginal

A marginal object from either inla or inla.hyperpar(), which is either list(x=c(), y=c()) with density values y at locations x, or a matrix(,n,2) for which the density values are the second column and the locations in the first column. Theinla.hpdmarginal()-function assumes a unimodal density.
log

Return density or interpolated density in log-scale?
extrapolate

How much to extrapolate on each side when computing the interpolation. In fraction of the range.
keep.type

If FALSE then return a list(x=, y=), otherwise if TRUE, then return a matrix if the input is a matrix
factor

The number of points after interpolation is factor times the original number of points; which is argument n in spline
fun

A (vectorised) function like function(x) exp(x) to compute the expectation against, or which define the transformation new = fun(old)
...

Further arguments to be passed to function which expectation is to be computed.
x

Evaluation points
q

Quantiles
normalize

Renormalise the density after interpolation?
len

Number of locations used to interpolate the distribution function.
p

Probabilities
n

The number of observations. If length(n) > 1, the length is taken to be the number required.
h.diff

The step-length for the numerical differeniation inside inla.tmarginal
method

Which method should be used to layout points for where the transformation is computed.
silent

Output the result visually (TRUE) or just through the call.

Value

inla.smarginal returns list=c(x=c(), y=c()) of interpolated values do extrapolation using the factor given, and the remaining function returns what they say they should do.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla(), inla.hyperpar()

Examples

## a simple linear regression example
 n = 10
 x = rnorm(n)
 sd = 0.1
 y = 1+x + rnorm(n,sd=sd)
 res = inla(y ~ 1 + x, data = data.frame(x,y),
            control.family=list(initial = log(1/sd^2L),fixed=TRUE))

 ## chose a marginal and compare the with the results computed by the
 ## inla-program
 r = res$summary.fixed["x",]
 m = res$marginals.fixed$x

 ## compute the 95% HPD interval
 inla.hpdmarginal(0.95, m)

 x = seq(-6, 6, length.out = 1000)
 y = dnorm(x)
 inla.hpdmarginal(0.95, list(x=x, y=y))

 ## compute the the density for exp(r), version 1
 r.exp = inla.tmarginal(exp, m)
 ## or version 2
 r.exp = inla.tmarginal(function(x) exp(x), m)

 ## to plot the marginal, we use the inla.smarginal, which interpolates (in
 ## log-scale). Compare with some samples.
 plot(inla.smarginal(m), type="l")
 s = inla.rmarginal(1000, m)
 hist(inla.rmarginal(1000, m), add=TRUE, prob=TRUE)
 lines(density(s), lty=2)

 m1 = inla.emarginal(function(x) x, m)
 m2 = inla.emarginal(function(x) x^2L, m)
 stdev = sqrt(m2 - m1^2L)
 q = inla.qmarginal(c(0.025,0.975), m)

 ## inla-program results
 print(r)

 ## inla.marginal-results (they shouldn't be perfect!)
 print(c(mean=m1, sd=stdev, "0.025quant" = q[1], "0.975quant" = q[2L]))
 ## using the buildt-in function
 inla.zmarginal(m)

Merge a mixture of inla-objects

Description

The function merge.inla implements method merge for inla-objects. merge.inla is a wrapper for the function inla.merge. The interface is slightly different, merge.inla is more tailored for interactive use, whereas inla.merge is better in general code.

inla.merge is intented for merging a mixture of inla-objects, each run with the same formula and settings, except for a set of hyperparameters, or other parameters in the model, that are fixed to different values. Using this function, we can then integrate over these hyperparameters using (unnormalized) integration weights prob. The main objects to be merged, are the summary statistics and marginal densities (like for hyperparameters, fixed, random, etc). Not all entries in the object can be merged, and by default these are inheritated from the first object in the list, while some are just set to NULL. Those objectes that are merged, will be listed if run with option verbose=TRUE.

Note that merging hyperparameter in the user-scale is prone to discretization error in general, so it is more stable to convert the marginal of the hyperparameter from the merged internal scale to the user-scale. (This is not done by this function.)

Usage

## S3 method for class 'inla'
merge(x, y, ..., prob = rep(1, length(list(x, y, ...))), verbose = FALSE)

inla.merge(loo, prob = rep(1, length(loo)), mc.cores = NULL, verbose = FALSE)

Arguments

x

An inla-object to be merged
y

An inla-object to be merged
...

Additional inla-objects to be merged
prob

The mixture of (possibly unnormalized) probabilities
verbose

Turn on verbose-output or not
loo

List of inla-objects to be merged
mc.cores

The number of cores to use in parallel::mclapply. If is.null(mc.cores), then check getOption("mc.cores") and inla.getOption("num.threads") in that order.

Value

A merged inla-object.

Author(s)

Havard Rue hrue@r-inla.org

Examples

set.seed(123)
 n = 100
 y = rnorm(n)
 y[1:10] = NA
 x = rnorm(n)
 z1 = runif(n)
 z2 = runif(n)*n
 idx = 1:n
 idx2 = 1:n
 lc1 = inla.make.lincomb(idx = c(1, 2, 3))
 names(lc1) = "lc1"
 lc2 = inla.make.lincomb(idx = c(0, 1, 2, 3))
 names(lc2) = "lc2"
 lc3 = inla.make.lincomb(idx = c(0, 0, 1, 2, 3))
 names(lc3) = "lc3"
 lc = c(lc1, lc2, lc3)
 rr = list()
 for (logprec in c(0, 1, 2))
     rr[[length(rr)+1]] = inla(y ~ 1 + x + f(idx, z1) + f(idx2, z2),
              lincomb = lc,
              control.family = list(hyper = list(prec = list(initial = logprec))),
              control.predictor = list(compute = TRUE, link = 1),
              data = data.frame(y, x, idx, idx2, z1, z2))
 r = inla.merge(rr, prob = seq_along(rr), verbose=TRUE)
 summary(r)

Interactive mesh building and diagnostics

Description

Interactively design and build a triangle mesh for use with SPDE models, and assess the finite element approximation errors. The R code needed to recreate the mesh outside the interactive Shiny app is also generated. Spatial objects can be imported from the global workspace.

Usage

meshbuilder()

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.mesh.2d(), inla.mesh.create()

Examples

## Not run: 
meshbuilder()

## End(Not run)

The Munich rent data

Description

The Munich rent data

Format

A data frame with 2035 observations on the following 17 variables.

rent

Net rent per square meter.

floor.size

Size of the flat in square meters.

year

Year of construction of the building in which the flat is located.

location

Location index (in terms of subquarters).

Gute.Wohnlage

Dummy variable for good locations / good neighborhoods.

Beste.Wohnlage

Dummy variable for very good locations / very good neighborhoods.

Keine.Wwv

Dummy for absence of warm water supply.

Keine.Zh

Dummy for absence of central heating system.

Kein.Badkach

Dummy for absence of flagging in the bathroom.

Besond.Bad

Dummy for special features of the bathroom.

Gehobene.Kueche

Dummy for more refined kitchen equipment.

zim1

Dummy for a flat with 1 room.

zim2

Dummy for a flat with 2 rooms.

zim3

Dummy for a flat with 3 rooms.

zim4

Dummy for a flat with 4 rooms.

zim5

Dummy for a flat with 5 rooms.

zim6

Dummy for a flat with 6 rooms.

Source

See Rue and Held (2005), Chapter 4.

References

Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall

The North West England map

Description

This map is used in association to the Leukemia data from Henderson et al (2003); see source.

Format

A SpatialPolygons object.

Source

This map are used to analyse the Leukaemia dataset from

Henderson, R. and Shimakura, S. and Gorst, D., 2002, Modeling spatial variation in leukemia survival data, JASA, 97, 460, 965–972.

Examples

data(Leuk)
plot(nwEngland)

~~ data name/kind ... ~~

Description

~~ A concise (1-5 lines) description of the dataset. ~~

Format

A data frame with 544 observations on the following 3 variables.

region

a numeric vector

E

a numeric vector

Y

a numeric vector

References

Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall

Parameter settings for inla.spde2.matern models.

Description

Construct parameter settings for inla.spde2.matern models.

Usage

param2.matern.orig(
  mesh,
  alpha = 2,
  B.tau = matrix(c(0, 1, 0), 1, 3),
  B.kappa = matrix(c(0, 0, 1), 1, 3),
  prior.variance.nominal = 1,
  prior.range.nominal = NULL,
  prior.tau = NULL,
  prior.kappa = NULL,
  theta.prior.mean = NULL,
  theta.prior.prec = 0.1
)

Arguments

mesh

The mesh to build the model on, as an fmesher::fm_mesh_2d() object, or other fmesher mesh supporting fmesher::fm_dof(), fmesher::fm_manifold_dim() and fmesher::fm_dof().
alpha

Fractional operator order, 0<α≤20<\alpha\leq 2 supported. (ν=α−d/2\nu=\alpha-d/2)
B.tau

Matrix with specification of log-linear model for τ\tau.
B.kappa

Matrix with specification of log-linear model for κ\kappa.
prior.variance.nominal

Nominal prior mean for the field variance
prior.range.nominal

Nominal prior mean for the spatial range
prior.tau

Prior mean for tau (overrides prior.variance.nominal)
prior.kappa

Prior mean for kappa (overrides prior.range.nominal)
theta.prior.mean

(overrides ⁠prior.*⁠)
theta.prior.prec

Scalar, vector or matrix, specifying the joint prior precision for thetatheta.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

inla.spde2.matern()

Describe and check the PARDISO support in R-INLA

Description

inla.pardiso() describes the PARDISO support in R-INLA, how to get the license key and enable it in the R-INLA package. inla.pardiso.check() check if the PARDISO support is working.

Usage

inla.pardiso()

inla.pardiso.check()

Author(s)

Havard Rue hrue@r-inla.org

Utility functions for the PC prior for the alpha parameter in the Weibull likelihood

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the alpha parameter in the Weibull likelihood

Usage

inla.pc.ralphaw(n, lambda = 5)

inla.pc.dalphaw(alpha, lambda = 5, log = FALSE)

inla.pc.qalphaw(p, lambda = 5)

inla.pc.palphaw(q, lambda = 5)

Arguments

n

Number of observations
lambda

The rate parameter in the PC-prior
alpha

Vector of evaluation points, where alpha>0.
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

This gives the PC prior for the alpha parameter for the Weibull likelihood, where alpha=1 is the base model.

Value

inla.pc.dalphaw gives the density, inla.pc.palphaw gives the distribution function, inla.pc.qalphaw gives the quantile function, and inla.pc.ralphaw generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.alphaw")

Examples

x = inla.pc.ralphaw(100,  lambda = 5)
 d = inla.pc.dalphaw(x, lambda = 5)
 x = inla.pc.qalphaw(0.5, lambda = 5)
 inla.pc.palphaw(x, lambda = 5)

Utility functions for the PC prior for a an AR(p) model

Description

Functions to evaluate and sample from the PC prior for an AR(p) model

Usage

inla.pc.ar.rpacf(n = 1, p, lambda = 1)

inla.pc.ar.dpacf(pac, lambda = 1, log = TRUE)

Arguments

n

Number of observations
p

The order of the AR-model
lambda

The rate parameter in the prior
pac

A vector of partial autocorrelation coefficients
log

Logical. Return the density in natural or log-scale.

Value

inla.pc.ar.rpac generate samples from the prior, returning a matrix where each row is a sample of theta. inla.pc.ar.dpac evaluates the density of pac. Use inla.ar.pacf2phi, inla.ar.phi2pacf, inla.ar.pacf2acf and inla.ar.acf2pacf to convert between various parameterisations.

Author(s)

Havard Rue hrue@r-inla.org

Utility functions for the PC prior for correlation in AR(1)

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the correlation in the Gaussian AR(1) model where the base-model is zero correlation.

Usage

inla.pc.rcor0(n, u, alpha, lambda)

inla.pc.dcor0(cor, u, alpha, lambda, log = FALSE)

inla.pc.qcor0(p, u, alpha, lambda)

inla.pc.pcor0(q, u, alpha, lambda)

Arguments

n

Number of observations
u

The upper limit (see Details)
alpha

The probability going above the upper limit (see Details)
lambda

The rate parameter (see Details)
cor

Vector of correlations
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

The statement ⁠Prob(|cor| > u) = alpha⁠ is used to determine lambda unless lambda is given. Either lambda must be given, or u AND alpha. The density is symmetric around zero.

Value

inla.pc.dcor0 gives the density, inla.pc.pcor0 gives the distribution function, inla.pc.qcor0 gives the quantile function, and inla.pc.rcor0 generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.rho0")

Examples

cor = inla.pc.rcor0(100,  lambda = 1)
 d = inla.pc.dcor0(cor, lambda = 1)
 cor = inla.pc.qcor0(c(0.3, 0.7), u = 0.5, alpha=0.01)
 inla.pc.pcor0(cor, u = 0.5, alpha=0.01)

Utility functions for the PC prior for correlation in AR(1)

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the correlation in the Gaussian AR(1) model where the base-model is correlation one.

Usage

inla.pc.rcor1(n, u, alpha, lambda)

inla.pc.dcor1(cor, u, alpha, lambda, log = FALSE)

inla.pc.qcor1(p, u, alpha, lambda)

inla.pc.pcor1(q, u, alpha, lambda)

Arguments

n

Number of observations
u

The upper limit (see Details)
alpha

The probability going above the upper limit (see Details)
lambda

The rate parameter (see Details)
cor

Vector of correlations
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

The statement Prob(cor > u) = alpha is used to determine lambda unless lambda is given. Either lambda must be given, or u AND alpha.

Value

inla.pc.dcor1 gives the density, inla.pc.pcor1 gives the distribution function, inla.pc.qcor1 gives the quantile function, and inla.pc.rcor1 generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.rho1")

Examples

cor = inla.pc.rcor1(100,  lambda = 1)
 d = inla.pc.dcor1(cor, lambda = 1)
 cor = inla.pc.qcor1(c(0.3, 0.7), u = 0.5, alpha=0.75)
 inla.pc.pcor1(cor, u = 0.5, alpha=0.75)

Utility functions for the PC prior for a correlation matrix

Description

Functions to evaluate and sample from the PC prior for a correlation matrix.

The parameterisation of a correlation matrix of dimension p has dim parameters: theta which are in the interval -pi to pi. The alternative parameterisation is through the off-diagonal elements r of the correlation matrix R. The functions ⁠inla.pc.cormat.<A>2<B>⁠ convert between parameterisations ⁠<A>⁠ to parameterisations ⁠<B>⁠, where both ⁠<A>⁠ and ⁠<B>⁠ are one of theta, r and R, and p and dim.

Usage

inla.pc.cormat.dim2p(dim)

inla.pc.cormat.p2dim(p)

inla.pc.cormat.theta2R(theta)

inla.pc.cormat.R2theta(R)

inla.pc.cormat.r2R(r)

inla.pc.cormat.R2r(R)

inla.pc.cormat.r2theta(r)

inla.pc.cormat.theta2r(theta)

inla.pc.cormat.permute(R)

inla.pc.cormat.rtheta(n = 1, p, lambda = 1)

inla.pc.cormat.dtheta(theta, lambda = 1, log = FALSE)

Arguments

dim

The dimension of theta, the parameterisatin of the correlation matrix
p

The dimension the correlation matrix
theta

A vector of parameters for the correlation matrix
R

A correlation matrix
r

The off diagonal elements of a correlation matrix
n

Number of observations
lambda

The rate parameter in the prior
log

Logical. Return the density in natural or log-scale.

Value

inla.pc.cormat.rtheta generate samples from the prior, returning a matrix where each row is a sample of theta. inla.pc.cormat.dtheta evaluates the density of theta. inla.pc.cormat.permute randomly permutes a correlation matrix, which is useful if an exchangable sample of a correlation matrix is required.

Author(s)

Havard Rue hrue@r-inla.org

Examples

p = 4
  print(paste("theta has length", inla.pc.cormat.p2dim(p)))
  theta = inla.pc.cormat.rtheta(n=1, p=4, lambda = 1)
  print("sample theta:")
  print(theta)
  print(paste("log.dens", inla.pc.cormat.dtheta(theta, log=TRUE)))
  print("r:")
  r = inla.pc.cormat.theta2r(theta)
  print(r)
  print("A sample from the non-exchangable prior, R:")
  R = inla.pc.cormat.r2R(r)
  print(R)
  print("A sample from the exchangable prior, R:")
  R = inla.pc.cormat.permute(R)
  print(R)

PC-prior for dof in a standardized Student-t

Description

A function to evaluate the PC-prior for the degrees of freedom in a standardized Student-t distribution

Usage

inla.pc.ddof(dof, lambda, u, alpha, log = FALSE)

Arguments

dof

Degrees of freedom
lambda

The optional value of lambda, instead of defining it implicitely through u and alpha
u

The upper value of dof used to elicitate lambda, Prob(dof < u) = alpha
alpha

The probability alpha used to elicitate lambda
log

Logical. Return the density or the log-density

Details

These functions implements the PC-prior for the dof in a standardized Student-t distribution (ie. with unit variance and dof > 2). Either lambda, or u AND alpha must be given. Due the internal tabulation, dof must be larger than 2.0025.

Value

inla.pc.ddof returns the prior density for given dof.

Author(s)

Havard Rue hrue@r-inla.org

Utility functions for the PC prior for Gamma(1/a, 1/a)

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for Gamma(1/a, 1/a)

Usage

inla.pc.rgamma(n, lambda = 1)

inla.pc.dgamma(x, lambda = 1, log = FALSE)

inla.pc.qgamma(p, lambda = 1)

inla.pc.pgamma(q, lambda = 1)

Arguments

n

Number of observations
lambda

The rate parameter (see Details)
x

Evaluation points
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

This gives the PC prior for the Gamma(1/a, 1/a) case, where a=0 is the base model.

Value

inla.pc.dgamma gives the density, inla.pc.pgamma gives the distribution function, inla.pc.qgamma gives the quantile function, and inla.pc.rgamma generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.gamma")

Examples

x = inla.pc.rgamma(100,  lambda = 1)
 d = inla.pc.dgamma(x, lambda = 1)
 x = inla.pc.qgamma(0.5, lambda = 1)
 inla.pc.pgamma(x, lambda = 1)

Utility functions for the PC prior for the gammacount likelihood

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the gammacount likelihood

Usage

inla.pc.rgammacount(n, lambda = 1)

inla.pc.dgammacount(x, lambda = 1, log = FALSE)

inla.pc.qgammacount(p, lambda = 1)

inla.pc.pgammacount(q, lambda = 1)

Arguments

n

Number of observations
lambda

The rate parameter (see Details)
x

Evaluation points
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

This gives the PC prior for the gammacount likelihood, which is the PC prior for a in Gamma(a, 1) where Gamma(1, 1) is the base model.

Value

inla.pc.dgammacount gives the density, inla.pc.pgammacount gives the distribution function, inla.pc.qgammacount gives the quantile function, and inla.pc.rgammacount generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.gammacount")

Examples

x = inla.pc.rgammacount(100,  lambda = 1)
 d = inla.pc.dgammacount(x, lambda = 1)
 x = inla.pc.qgammacount(0.5, lambda = 1)
 inla.pc.pgammacount(x, lambda = 1)

Utility functions for the PC prior for the tail parameter in the GEV likelihood

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the tail parameter in the GEV likelihood

Usage

inla.pc.rgevtail(n, lambda = 7)

inla.pc.dgevtail(xi, lambda = 7, log = FALSE)

inla.pc.qgevtail(p, lambda = 7)

inla.pc.pgevtail(q, lambda = 7)

Arguments

n

Number of observations
lambda

The rate parameter in the PC-prior
xi

Vector of evaluation points, where ⁠1>xi>0⁠.
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

This gives the PC prior for the tail parameter for the GEV likelihood, where xi=0 is the base model.

Value

inla.pc.dgevtail gives the density, inla.pc.pgevtail gives the distribution function, inla.pc.qgevtail gives the quantile function, and inla.pc.rgevtail generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.gevtail")

Examples

xi = inla.pc.rgevtail(100,  lambda = 7)
 d = inla.pc.dgevtail(xi, lambda = 7)
 xi = inla.pc.qgevtail(0.5, lambda = 7)
 inla.pc.pgevtail(xi, lambda = 7)

Multivariate PC priors

Description

Functions to evaluate and simulate from multivariate PC priors: The simplex and sphere case

Usage

inla.pc.multvar.h.default(x, inverse = FALSE, derivative = FALSE)

inla.pc.multvar.simplex.r(
  n = NULL,
  lambda = 1,
  h = inla.pc.multvar.h.default,
  b = NULL
)

inla.pc.multvar.simplex.d(
  x = NULL,
  lambda = 1,
  log = FALSE,
  h = inla.pc.multvar.h.default,
  b = NULL
)

inla.pc.multvar.sphere.r(
  n = NULL,
  lambda = 1,
  h = inla.pc.multvar.h.default,
  H = NULL
)

inla.pc.multvar.sphere.d(
  x = NULL,
  lambda = 1,
  log = FALSE,
  h = inla.pc.multvar.h.default,
  H = NULL
)

Arguments

x

Samples to evaluate. If input is a matrix then each row is a sample. If input is a vector then this is the sample.
inverse

Compute the inverse of the h()-function.
derivative

Compute the derivative of the h()-function. (derivative of the inverse function is not used).
n

Number of samples to generate.
lambda

The lambda-parameter in the PC-prior.
h

The h()-function, defaults to inla.pc.multvar.h.default. See that code for an example of how to write a user-specific function.
b

The b-vector (gradient) in the expression for the simplex option, ⁠d(xi) = h(b^T xi)⁠
log

Evaluate the density in log-scale or ordinary scale.
H

The H(essian)-matrix in the expression for the sphere option, ⁠d(xi) = h(1/2 *xi^T H xi)⁠. If H is a vector, then it is interpreted as the diagonal of a (sparse) diagonal matrix.

Details

These functions implements multivariate PC-priors of the simplex and sphere type.

Value

inla.pc.multvar.simplex.r generate samples from the simplex case, and inla.pc.multvar.simplex.d evaluate the density. inla.pc.multvar.sphere.r generate samples from the sphere case, and inla.pc.multvar.sphere.d evaluate the density. inla.pc.multvar.h.default implements the default h()-function and illustrate how to code your own specific one, if needed.

Author(s)

Havard Rue hrue@r-inla.org

Utility functions for the PC prior for the precision

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the precision in the Gaussian distribution.

Usage

inla.pc.rprec(n, u, alpha, lambda)

inla.pc.dprec(prec, u, alpha, lambda, log = FALSE)

inla.pc.qprec(p, u, alpha, lambda)

inla.pc.pprec(q, u, alpha, lambda)

Arguments

n

Number of observations
u

The upper limit (see Details)
alpha

The probability going above the upper limit (see Details)
lambda

The rate parameter (see Details)
prec

Vector of precisions
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

The statement Prob(1/sqrt(prec) > u) = alpha is used to determine lambda unless lambda is given. Either lambda must be given, or u AND alpha.

Value

inla.pc.dprec gives the density, inla.pc.pprec gives the distribution function, inla.pc.qprec gives the quantile function, and inla.pc.rprec generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.prec")

Examples

prec = inla.pc.rprec(100,  lambda = 1)
 d = inla.pc.dprec(prec, lambda = 1)
 prec = inla.pc.qprec(0.5, u = 1, alpha=0.01)
 inla.pc.pprec(prec, u = 1, alpha=0.01)

Utility functions for the PC prior for skewness in the skew-normal linkfunction and likelihood

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the skewness in the skew-normal link-function and likelihood

Usage

inla.pc.rsn(n, lambda = 40)

inla.pc.dsn(skew, lambda = 40, log = FALSE)

inla.pc.qsn(p, lambda = 40)

inla.pc.psn(q, lambda = 40)

Arguments

n

number of observations
lambda

the rate parameter in the PC prior
skew

vector of evaluation points
log

logical. return the density in natural or log-scale.
p

vector of probabilities
q

vector of quantiles

Details

Defines the PC prior for the skewness for the skew-normal linkfunction and likelihood, where skew=0 is the base model. The skewness range from -0.99527... to 0.99527.... ca.

Value

inla.pc.dsn gives the density, inla.pc.psn gives the distribution function, inla.pc.qsn gives the quantile function, and inla.pc.rsn generates random deviates.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.doc("pc.sn")

Examples

x = inla.pc.rsn(100,  lambda = 40)
 d = inla.pc.dsn(x, lambda = 40)
 x = inla.pc.qsn(0.5, lambda = 40)
 inla.pc.psn(x, lambda = 40)

Utility functions for the PC prior for the precision

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the precision in the Gaussian distribution.

Usage

inla.pc.rvm0(n, u, alpha, lambda)

inla.pc.dvm0(k, u, alpha, lambda, log = FALSE)

inla.pc.qvm0(p, u, alpha, lambda, len = 2048L)

inla.pc.pvm0(q, u, alpha, lambda, log = FALSE)

Arguments

n

Number of observations
u

The upper limit (0 < u < 2*pi). The small values of u indicate a high concentration to a point mass, whilst large values of u mean that the user believes the data spread widely.
alpha

The probability going above the upper limit (the probability assigned to the event Prob(2*pi/(1+k) > u)).
lambda

The rate parameter (see Details)
k

The concentration of von Mises distribution
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities
q

Vector of quantiles

Details

The statement Prob(2*pi/(1+k) > u) = alpha is used to determine lambda unless lambda is given. Either lambda must be given, or u AND alpha.

Due to limitations in handling extreme values for special functions, the output of these functions may exhibit bias when the input parameter values are either excessively large or very close to zero.

Value

inla.pc.dvm0 gives the density, inla.pc.pvm0 gives the distribution function, inla.pc.qvm0 gives the quantile function, and inla.pc.rvm0 generates random deviates.

Author(s)

Xiang Ye xiang.ye@kaust.edu.sa

See Also

inla.doc("pc.vm0")

Examples

k = inla.pc.rvm0(100,  lambda = 1)
 d = inla.pc.dvm0(1, lambda = 1)
 k = inla.pc.qvm0(0.5, u = 1, alpha=0.01)
 inla.pc.pvm0(5, u = 1, alpha=0.01)

Utility functions for the PC prior for the concentration of von Mises distribution with point mass base model

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the concentration in the von Mises distribution.

Usage

inla.pc.rvminf(n, u, alpha, lambda)

inla.pc.dvminf(k, u, alpha, lambda, log = FALSE)

inla.pc.qvminf(p, u, alpha, lambda, len = 2048L)

inla.pc.pvminf(q, u, alpha, lambda, log = FALSE)

Arguments

n

Number of observations
u

The upper limit (0 < u < 2*pi). The small values of u indicate a high concentration to a point mass, whilst large values of u mean that the user believes the data spread widely.
alpha

The probability going above the upper limit (the probability assigned to the event Prob(2*pi/(1+k) > u)).
lambda

The rate parameter.
k

The concentration of von Mises distribution
log

Logical. Return the density in natural or log-scale.
p

Vector of probabilities.
q

Vector of quantiles.

Details

The statement Prob(2*pi/(1+k) > u) = alpha is used to determine lambda unless lambda is given. Either lambda must be given, or u AND alpha.

Due to limitations in handling extreme values for special functions, the output of these functions may exhibit bias when the input parameter values are either excessively large or very close to zero.

Value

inla.pc.dvminf gives the density, inla.pc.pvminf gives the distribution function, inla.pc.qvminf gives the quantile function, and inla.pc.rvminf generates random deviates.

Author(s)

Xiang Ye xiang.ye@kaust.edu.sa

See Also

inla.doc("pc.vminf")

Examples

k = inla.pc.rvminf(100,  lambda = 1)
 d = inla.pc.dvminf(1, lambda = 1)
 k = inla.pc.qvminf(0.5, u = 1, alpha=0.01)
 inla.pc.pvminf(5, u = 1, alpha=0.01)

Default INLA plotting

Description

Takes an inla object produced by inla and plot the results

Usage

## S3 method for class 'inla'
plot(
  x,
  plot.fixed.effects = TRUE,
  plot.lincomb = TRUE,
  plot.random.effects = TRUE,
  plot.hyperparameters = TRUE,
  plot.predictor = TRUE,
  plot.q = TRUE,
  plot.cpo = TRUE,
  plot.prior = FALSE,
  plot.opt.trace = FALSE,
  single = FALSE,
  postscript = FALSE,
  pdf = FALSE,
  prefix = "inla.plots/figure-",
  intern = FALSE,
  debug = FALSE,
  cex = 1.75,
  ...
)

Arguments

x

A fitted inla object produced by inla
plot.fixed.effects

Boolean indicating if posterior marginals for the fixed effects in the model should be plotted
plot.lincomb

Boolean indicating if posterior marginals for the linear combinations should be plotted
plot.random.effects

Boolean indicating if posterior mean and quantiles for the random effects in the model should be plotted
plot.hyperparameters

Boolean indicating if posterior marginals for the hyperparameters in the model should be plotted
plot.predictor

Boolean indicating if posterior mean and quantiles for the linear predictor in the model should be plotted
plot.q

Boolean indicating if precision matrix should be displayed
plot.cpo

Boolean indicating if CPO/PIT valuesshould be plotted
plot.prior

Plot also the prior density for the hyperparameters
plot.opt.trace

Plot optimization trace
single

Boolean indicating if there should be more than one plot per page (FALSE) or just one (TRUE)
postscript

Boolean indicating if postscript files should be produced instead
pdf

Boolean indicating if PDF files should be produced instead
prefix

The prefix for the created files. Additional numbering and suffix is added.
intern

Plot also the hyperparameters in its internal scale.
debug

Write some debug information
cex

The cex parameter in par(). If set to NULL or 0, then default values will be used for graphics parameters
...

Additional arguments to postscript(), pdf() or dev.new().

Value

The return value is a list of the files created (if any).

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla()

Examples

## Not run: 
result = inla(...)
plot(result)
plot(result, single = TRUE, plot.prior = TRUE)
plot(result, single = TRUE, pdf = TRUE, paper = "a4")
   
## End(Not run)

Draw a triangulation mesh object

Description

[Deprecated] Use fmesher::plot.fm_mesh_2d() or fmesher::plot_rgl() instead.

Plots an inla.mesh() object using either standard graphics or with rgl.

Usage

## S3 method for class 'inla.mesh'
plot(
  x,
  col = "white",
  t.sub = 1:nrow(mesh$graph$tv),
  add = FALSE,
  lwd = 1,
  xlim = range(mesh$loc[, 1]),
  ylim = range(mesh$loc[, 2]),
  main = NULL,
  rgl = FALSE,
  size = 2,
  draw.vertices = FALSE,
  vertex.color = "black",
  draw.edges = TRUE,
  edge.color = rgb(0.3, 0.3, 0.3),
  draw.segments = draw.edges,
  ...
)

Arguments

x

An inla.mesh() object.
col

Color specification. A single named color, a vector of scalar values, or a matrix of RGB values. Requires rgl=TRUE.
t.sub

Optional triangle index subset to be drawn.
add

If TRUE, adds to the current plot instead of starting a new one.
lwd

Line width for triangle edges.
xlim

X-axis limits.
ylim

Y-axis limits.
main

The main plot title. If not specified, a default title is generated based on the mesh type.
rgl

When TRUE, generates an rgl plot instead of a generic graphics plot. Allows 3D plotting and color surface plotting.
size

Size of vertex points in rgl plotting. See rgl.material.
draw.vertices

If TRUE, draw triengle vertices.
vertex.color

Color specification for all vertices.
draw.edges

If TRUE, draw triangle edges.
edge.color

Color specification for all edges.
draw.segments

If TRUE, draw boundary and interior constraint edges more prominently.
...

Further graphics parameters, interpreted by the respective plotting systems.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

plot.inla.trimesh()

Examples

mesh <- inla.mesh.create(globe = 10)
plot(mesh)

if (require(rgl)) {
  plot(mesh, rgl = TRUE, col = mesh$loc[, 1])
}

Low level triangulation mesh plotting

Description

[Deprecated] Use fmesher::plot_rgl() instead.

Plots a triangulation mesh using rgl.

Usage

## S3 method for class 'inla.trimesh'
plot(
  x,
  S,
  color = NULL,
  color.axis = NULL,
  color.n = 512,
  color.palette = cm.colors,
  color.truncate = FALSE,
  alpha = NULL,
  lwd = 1,
  specular = "black",
  draw.vertices = TRUE,
  draw.edges = TRUE,
  edge.color = rgb(0.3, 0.3, 0.3),
  ...
)

Arguments

x

A 3-column triangle-to-vertex index map matrix.
S

A 3-column vertex coordinate matrix.
color

Color specification. A single named color, a vector of scalar values, or a matrix of RGB values.
color.axis

The min/max limit values for the color mapping.
color.n

The number of colors to use in the color palette.
color.palette

A color palette function.
color.truncate

If TRUE, truncate the colors at the color axis limits.
alpha

Transparency/opaqueness values. See rgl.material.
lwd

Line width for edges. See rgl.material.
specular

Specular color. See rgl.material.
draw.vertices

If TRUE, draw triangle vertices.
draw.edges

If TRUE, draw triangle edges.
edge.color

Edge color specification.
...

Additional parameters passed to and from other methods.

Author(s)

Finn Lindgren finn.lindgren@gmail.com

See Also

plot.inla.mesh()

The PRborder data

Description

A data matrix with Longitude and Latitude coordinates for the boundary of Parana State.

Format

Longtiude

The Longtiude coordinate

Latitude

The Latitude coordinate

See Also

PRprec

Print an INLA fit

Description

Print an INLA fit

Usage

## S3 method for class 'inla'
print(x, digits = 3L, ...)

Arguments

x

An inla-object (output from an inla()-call).
digits

Number of digits to print
...

other arguments.

Value

None

Author(s)

Havard Rue

See Also

inla()

The PRprec data

Description

A data frame with daily rainfall in the Parana State.

Format

A data frame .... TODO

Altitude

TODO

Latitude

TODO

Longitude

TODO

d0101

Daily rainfall at day "mmdd"

d0102

Daily rainfall at day "mmdd"

d0103

Daily rainfall at day "mmdd"

d0104

Daily rainfall at day "mmdd"

d0105

Daily rainfall at day "mmdd"

d0106

Daily rainfall at day "mmdd"

d0107

Daily rainfall at day "mmdd"

d0108

Daily rainfall at day "mmdd"

d0109

Daily rainfall at day "mmdd"

d0110

Daily rainfall at day "mmdd"

d0111

Daily rainfall at day "mmdd"

d0112

Daily rainfall at day "mmdd"

d0113

Daily rainfall at day "mmdd"

d0114

Daily rainfall at day "mmdd"

d0115

Daily rainfall at day "mmdd"

d0116

Daily rainfall at day "mmdd"

d0117

Daily rainfall at day "mmdd"

d0118

Daily rainfall at day "mmdd"

d0119

Daily rainfall at day "mmdd"

d0120

Daily rainfall at day "mmdd"

d0121

Daily rainfall at day "mmdd"

d0122

Daily rainfall at day "mmdd"

d0123

Daily rainfall at day "mmdd"

d0124

Daily rainfall at day "mmdd"

d0125

Daily rainfall at day "mmdd"

d0126

Daily rainfall at day "mmdd"

d0127

Daily rainfall at day "mmdd"

d0128

Daily rainfall at day "mmdd"

d0129

Daily rainfall at day "mmdd"

d0130

Daily rainfall at day "mmdd"

d0131

Daily rainfall at day "mmdd"

d0201

Daily rainfall at day "mmdd"

d0202

Daily rainfall at day "mmdd"

d0203

Daily rainfall at day "mmdd"

d0204

Daily rainfall at day "mmdd"

d0205

Daily rainfall at day "mmdd"

d0206

Daily rainfall at day "mmdd"

d0207

Daily rainfall at day "mmdd"

d0208

Daily rainfall at day "mmdd"

d0209

Daily rainfall at day "mmdd"

d0210

Daily rainfall at day "mmdd"

d0211

Daily rainfall at day "mmdd"

d0212

Daily rainfall at day "mmdd"

d0213

Daily rainfall at day "mmdd"

d0214

Daily rainfall at day "mmdd"

d0215

Daily rainfall at day "mmdd"

d0216

Daily rainfall at day "mmdd"

d0217

Daily rainfall at day "mmdd"

d0218

Daily rainfall at day "mmdd"

d0219

Daily rainfall at day "mmdd"

d0220

Daily rainfall at day "mmdd"

d0221

Daily rainfall at day "mmdd"

d0222

Daily rainfall at day "mmdd"

d0223

Daily rainfall at day "mmdd"

d0224

Daily rainfall at day "mmdd"

d0225

Daily rainfall at day "mmdd"

d0226

Daily rainfall at day "mmdd"

d0227

Daily rainfall at day "mmdd"

d0228

Daily rainfall at day "mmdd"

d0301

Daily rainfall at day "mmdd"

d0302

Daily rainfall at day "mmdd"

d0303

Daily rainfall at day "mmdd"

d0304

Daily rainfall at day "mmdd"

d0305

Daily rainfall at day "mmdd"

d0306

Daily rainfall at day "mmdd"

d0307

Daily rainfall at day "mmdd"

d0308

Daily rainfall at day "mmdd"

d0309

Daily rainfall at day "mmdd"

d0310

Daily rainfall at day "mmdd"

d0311

Daily rainfall at day "mmdd"

d0312

Daily rainfall at day "mmdd"

d0313

Daily rainfall at day "mmdd"

d0314

Daily rainfall at day "mmdd"

d0315

Daily rainfall at day "mmdd"

d0316

Daily rainfall at day "mmdd"

d0317

Daily rainfall at day "mmdd"

d0318

Daily rainfall at day "mmdd"

d0319

Daily rainfall at day "mmdd"

d0320

Daily rainfall at day "mmdd"

d0321

Daily rainfall at day "mmdd"

d0322

Daily rainfall at day "mmdd"

d0323

Daily rainfall at day "mmdd"

d0324

Daily rainfall at day "mmdd"

d0325

Daily rainfall at day "mmdd"

d0326

Daily rainfall at day "mmdd"

d0327

Daily rainfall at day "mmdd"

d0328

Daily rainfall at day "mmdd"

d0329

Daily rainfall at day "mmdd"

d0330

Daily rainfall at day "mmdd"

d0331

Daily rainfall at day "mmdd"

d0401

Daily rainfall at day "mmdd"

d0402

Daily rainfall at day "mmdd"

d0403

Daily rainfall at day "mmdd"

d0404

Daily rainfall at day "mmdd"

d0405

Daily rainfall at day "mmdd"

d0406

Daily rainfall at day "mmdd"

d0407

Daily rainfall at day "mmdd"

d0408

Daily rainfall at day "mmdd"

d0409

Daily rainfall at day "mmdd"

d0410

Daily rainfall at day "mmdd"

d0411

Daily rainfall at day "mmdd"

d0412

Daily rainfall at day "mmdd"

d0413

Daily rainfall at day "mmdd"

d0414

Daily rainfall at day "mmdd"

d0415

Daily rainfall at day "mmdd"

d0416

Daily rainfall at day "mmdd"

d0417

Daily rainfall at day "mmdd"

d0418

Daily rainfall at day "mmdd"

d0419

Daily rainfall at day "mmdd"

d0420

Daily rainfall at day "mmdd"

d0421

Daily rainfall at day "mmdd"

d0422

Daily rainfall at day "mmdd"

d0423

Daily rainfall at day "mmdd"

d0424

Daily rainfall at day "mmdd"

d0425

Daily rainfall at day "mmdd"

d0426

Daily rainfall at day "mmdd"

d0427

Daily rainfall at day "mmdd"

d0428

Daily rainfall at day "mmdd"

d0429

Daily rainfall at day "mmdd"

d0430

Daily rainfall at day "mmdd"

d0501

Daily rainfall at day "mmdd"

d0502

Daily rainfall at day "mmdd"

d0503

Daily rainfall at day "mmdd"

d0504

Daily rainfall at day "mmdd"

d0505

Daily rainfall at day "mmdd"

d0506

Daily rainfall at day "mmdd"

d0507

Daily rainfall at day "mmdd"

d0508

Daily rainfall at day "mmdd"

d0509

Daily rainfall at day "mmdd"

d0510

Daily rainfall at day "mmdd"

d0511

Daily rainfall at day "mmdd"

d0512

Daily rainfall at day "mmdd"

d0513

Daily rainfall at day "mmdd"

d0514

Daily rainfall at day "mmdd"

d0515

Daily rainfall at day "mmdd"

d0516

Daily rainfall at day "mmdd"

d0517

Daily rainfall at day "mmdd"

d0518

Daily rainfall at day "mmdd"

d0519

Daily rainfall at day "mmdd"

d0520

Daily rainfall at day "mmdd"

d0521

Daily rainfall at day "mmdd"

d0522

Daily rainfall at day "mmdd"

d0523

Daily rainfall at day "mmdd"

d0524

Daily rainfall at day "mmdd"

d0525

Daily rainfall at day "mmdd"

d0526

Daily rainfall at day "mmdd"

d0527

Daily rainfall at day "mmdd"

d0528

Daily rainfall at day "mmdd"

d0529

Daily rainfall at day "mmdd"

d0530

Daily rainfall at day "mmdd"

d0531

Daily rainfall at day "mmdd"

d0601

Daily rainfall at day "mmdd"

d0602

Daily rainfall at day "mmdd"

d0603

Daily rainfall at day "mmdd"

d0604

Daily rainfall at day "mmdd"

d0605

Daily rainfall at day "mmdd"

d0606

Daily rainfall at day "mmdd"

d0607

Daily rainfall at day "mmdd"

d0608

Daily rainfall at day "mmdd"

d0609

Daily rainfall at day "mmdd"

d0610

Daily rainfall at day "mmdd"

d0611

Daily rainfall at day "mmdd"

d0612

Daily rainfall at day "mmdd"

d0613

Daily rainfall at day "mmdd"

d0614

Daily rainfall at day "mmdd"

d0615

Daily rainfall at day "mmdd"

d0616

Daily rainfall at day "mmdd"

d0617

Daily rainfall at day "mmdd"

d0618

Daily rainfall at day "mmdd"

d0619

Daily rainfall at day "mmdd"

d0620

Daily rainfall at day "mmdd"

d0621

Daily rainfall at day "mmdd"

d0622

Daily rainfall at day "mmdd"

d0623

Daily rainfall at day "mmdd"

d0624

Daily rainfall at day "mmdd"

d0625

Daily rainfall at day "mmdd"

d0626

Daily rainfall at day "mmdd"

d0627

Daily rainfall at day "mmdd"

d0628

Daily rainfall at day "mmdd"

d0629

Daily rainfall at day "mmdd"

d0630

Daily rainfall at day "mmdd"

d0701

Daily rainfall at day "mmdd"

d0702

Daily rainfall at day "mmdd"

d0703

Daily rainfall at day "mmdd"

d0704

Daily rainfall at day "mmdd"

d0705

Daily rainfall at day "mmdd"

d0706

Daily rainfall at day "mmdd"

d0707

Daily rainfall at day "mmdd"

d0708

Daily rainfall at day "mmdd"

d0709

Daily rainfall at day "mmdd"

d0710

Daily rainfall at day "mmdd"

d0711

Daily rainfall at day "mmdd"

d0712

Daily rainfall at day "mmdd"

d0713

Daily rainfall at day "mmdd"

d0714

Daily rainfall at day "mmdd"

d0715

Daily rainfall at day "mmdd"

d0716

Daily rainfall at day "mmdd"

d0717

Daily rainfall at day "mmdd"

d0718

Daily rainfall at day "mmdd"

d0719

Daily rainfall at day "mmdd"

d0720

Daily rainfall at day "mmdd"

d0721

Daily rainfall at day "mmdd"

d0722

Daily rainfall at day "mmdd"

d0723

Daily rainfall at day "mmdd"

d0724

Daily rainfall at day "mmdd"

d0725

Daily rainfall at day "mmdd"

d0726

Daily rainfall at day "mmdd"

d0727

Daily rainfall at day "mmdd"

d0728

Daily rainfall at day "mmdd"

d0729

Daily rainfall at day "mmdd"

d0730

Daily rainfall at day "mmdd"

d0731

Daily rainfall at day "mmdd"

d0801

Daily rainfall at day "mmdd"

d0802

Daily rainfall at day "mmdd"

d0803

Daily rainfall at day "mmdd"

d0804

Daily rainfall at day "mmdd"

d0805

Daily rainfall at day "mmdd"

d0806

Daily rainfall at day "mmdd"

d0807

Daily rainfall at day "mmdd"

d0808

Daily rainfall at day "mmdd"

d0809

Daily rainfall at day "mmdd"

d0810

Daily rainfall at day "mmdd"

d0811

Daily rainfall at day "mmdd"

d0812

Daily rainfall at day "mmdd"

d0813

Daily rainfall at day "mmdd"

d0814

Daily rainfall at day "mmdd"

d0815

Daily rainfall at day "mmdd"

d0816

Daily rainfall at day "mmdd"

d0817

Daily rainfall at day "mmdd"

d0818

Daily rainfall at day "mmdd"

d0819

Daily rainfall at day "mmdd"

d0820

Daily rainfall at day "mmdd"

d0821

Daily rainfall at day "mmdd"

d0822

Daily rainfall at day "mmdd"

d0823

Daily rainfall at day "mmdd"

d0824

Daily rainfall at day "mmdd"

d0825

Daily rainfall at day "mmdd"

d0826

Daily rainfall at day "mmdd"

d0827

Daily rainfall at day "mmdd"

d0828

Daily rainfall at day "mmdd"

d0829

Daily rainfall at day "mmdd"

d0830

Daily rainfall at day "mmdd"

d0831

Daily rainfall at day "mmdd"

d0901

Daily rainfall at day "mmdd"

d0902

Daily rainfall at day "mmdd"

d0903

Daily rainfall at day "mmdd"

d0904

Daily rainfall at day "mmdd"

d0905

Daily rainfall at day "mmdd"

d0906

Daily rainfall at day "mmdd"

d0907

Daily rainfall at day "mmdd"

d0908

Daily rainfall at day "mmdd"

d0909

Daily rainfall at day "mmdd"

d0910

Daily rainfall at day "mmdd"

d0911

Daily rainfall at day "mmdd"

d0912

Daily rainfall at day "mmdd"

d0913

Daily rainfall at day "mmdd"

d0914

Daily rainfall at day "mmdd"

d0915

Daily rainfall at day "mmdd"

d0916

Daily rainfall at day "mmdd"

d0917

Daily rainfall at day "mmdd"

d0918

Daily rainfall at day "mmdd"

d0919

Daily rainfall at day "mmdd"

d0920

Daily rainfall at day "mmdd"

d0921

Daily rainfall at day "mmdd"

d0922

Daily rainfall at day "mmdd"

d0923

Daily rainfall at day "mmdd"

d0924

Daily rainfall at day "mmdd"

d0925

Daily rainfall at day "mmdd"

d0926

Daily rainfall at day "mmdd"

d0927

Daily rainfall at day "mmdd"

d0928

Daily rainfall at day "mmdd"

d0929

Daily rainfall at day "mmdd"

d0930

Daily rainfall at day "mmdd"

d1001

Daily rainfall at day "mmdd"

d1002

Daily rainfall at day "mmdd"

d1003

Daily rainfall at day "mmdd"

d1004

Daily rainfall at day "mmdd"

d1005

Daily rainfall at day "mmdd"

d1006

Daily rainfall at day "mmdd"

d1007

Daily rainfall at day "mmdd"

d1008

Daily rainfall at day "mmdd"

d1009

Daily rainfall at day "mmdd"

d1010

Daily rainfall at day "mmdd"

d1011

Daily rainfall at day "mmdd"

d1012

Daily rainfall at day "mmdd"

d1013

Daily rainfall at day "mmdd"

d1014

Daily rainfall at day "mmdd"

d1015

Daily rainfall at day "mmdd"

d1016

Daily rainfall at day "mmdd"

d1017

Daily rainfall at day "mmdd"

d1018

Daily rainfall at day "mmdd"

d1019

Daily rainfall at day "mmdd"

d1020

Daily rainfall at day "mmdd"

d1021

Daily rainfall at day "mmdd"

d1022

Daily rainfall at day "mmdd"

d1023

Daily rainfall at day "mmdd"

d1024

Daily rainfall at day "mmdd"

d1025

Daily rainfall at day "mmdd"

d1026

Daily rainfall at day "mmdd"

d1027

Daily rainfall at day "mmdd"

d1028

Daily rainfall at day "mmdd"

d1029

Daily rainfall at day "mmdd"

d1030

Daily rainfall at day "mmdd"

d1031

Daily rainfall at day "mmdd"

d1101

Daily rainfall at day "mmdd"

d1102

Daily rainfall at day "mmdd"

d1103

Daily rainfall at day "mmdd"

d1104

Daily rainfall at day "mmdd"

d1105

Daily rainfall at day "mmdd"

d1106

Daily rainfall at day "mmdd"

d1107

Daily rainfall at day "mmdd"

d1108

Daily rainfall at day "mmdd"

d1109

Daily rainfall at day "mmdd"

d1110

Daily rainfall at day "mmdd"

d1111

Daily rainfall at day "mmdd"

d1112

Daily rainfall at day "mmdd"

d1113

Daily rainfall at day "mmdd"

d1114

Daily rainfall at day "mmdd"

d1115

Daily rainfall at day "mmdd"

d1116

Daily rainfall at day "mmdd"

d1117

Daily rainfall at day "mmdd"

d1118

Daily rainfall at day "mmdd"

d1119

Daily rainfall at day "mmdd"

d1120

Daily rainfall at day "mmdd"

d1121

Daily rainfall at day "mmdd"

d1122

Daily rainfall at day "mmdd"

d1123

Daily rainfall at day "mmdd"

d1124

Daily rainfall at day "mmdd"

d1125

Daily rainfall at day "mmdd"

d1126

Daily rainfall at day "mmdd"

d1127

Daily rainfall at day "mmdd"

d1128

Daily rainfall at day "mmdd"

d1129

Daily rainfall at day "mmdd"

d1130

Daily rainfall at day "mmdd"

d1201

Daily rainfall at day "mmdd"

d1202

Daily rainfall at day "mmdd"

d1203

Daily rainfall at day "mmdd"

d1204

Daily rainfall at day "mmdd"

d1205

Daily rainfall at day "mmdd"

d1206

Daily rainfall at day "mmdd"

d1207

Daily rainfall at day "mmdd"

d1208

Daily rainfall at day "mmdd"

d1209

Daily rainfall at day "mmdd"

d1210

Daily rainfall at day "mmdd"

d1211

Daily rainfall at day "mmdd"

d1212

Daily rainfall at day "mmdd"

d1213

Daily rainfall at day "mmdd"

d1214

Daily rainfall at day "mmdd"

d1215

Daily rainfall at day "mmdd"

d1216

Daily rainfall at day "mmdd"

d1217

Daily rainfall at day "mmdd"

d1218

Daily rainfall at day "mmdd"

d1219

Daily rainfall at day "mmdd"

d1220

Daily rainfall at day "mmdd"

d1221

Daily rainfall at day "mmdd"

d1222

Daily rainfall at day "mmdd"

d1223

Daily rainfall at day "mmdd"

d1224

Daily rainfall at day "mmdd"

d1225

Daily rainfall at day "mmdd"

d1226

Daily rainfall at day "mmdd"

d1227

Daily rainfall at day "mmdd"

d1228

Daily rainfall at day "mmdd"

d1229

Daily rainfall at day "mmdd"

d1230

Daily rainfall at day "mmdd"

d1231

Daily rainfall at day "mmdd"

See Also

PRborder

Computes (parts of) the inverse of a SPD sparse matrix

Description

This routine use the GMRFLib implementation which compute parts of the inverse of a SPD sparse matrix. The diagonal and values for the neighbours in the inverse, are provided.

Usage

inla.qinv(Q, constr, reordering = INLA::inla.reorderings(), num.threads = NULL)

Arguments

Q

A SPD matrix, either as a (dense) matrix or sparseMatrix.
constr

Optional linear constraints; see ?INLA::f and argument extraconstr
reordering

The type of reordering algorithm to be used for TAUCS; either one of the names listed in inla.reorderings() or the output from inla.qreordering(Q). The default is "auto" which try several reordering algorithm and use the best one for this particular matrix.
num.threads

Maximum number of threads the inla-program will use, or as 'A:B' defining the number threads in the outer (A) and inner (B) layer for nested parallelism.

Value

inla.qinv returns a sparseMatrix of type dgTMatrix with the diagonal and values for the neigbours in the inverse. Note that the full inverse is NOT provided!

Author(s)

Havard Rue hrue@r-inla.org

Examples

## dense matrix example
 n = 10
 A = matrix(runif(n^2), n, n)
 Q = A %*% t(A)
 print(mean(abs(inla.qinv(Q) - solve(Q))))

 ## sparse matrix example
 rho = 0.9
 Q = toeplitz(c(1+rho^2, -rho,  rep(0, n-3), -rho)) / (1-rho^2)
 Q = inla.as.dgTMatrix(Q)
 Q.inv = inla.qinv(Q)

 ## compute the marginal variances as a vector from a precision matrix
 marginal.variances = diag(inla.qinv(Q))

 ## read the sparse matrix from a file in the 'i, j, value' format
 filename = tempfile()
 write(t(cbind(Q@i+1L,  Q@j+1L,  Q@x)), ncol=3, file=filename)
 Qinv = inla.qinv(filename)
 unlink(filename)

Compute the reordering using the GMRFLib implementation

Description

This function compute the reordering (or find the best reordering) using the GMRFLib implementation

Usage

inla.qreordering(graph, reordering = inla.reorderings())

Arguments

graph

A ⁠(inla-)graph⁠ object
reordering

The name of the reordering algorithm to be used; either one of the names listed in inla.reorderings(). The default is "auto" which try several reordering algorithm and use the best one for this particular matrix.

Value

inla.qreordering returns a list with the name of the reordering algorithm used or found, the reordering code for the reordering algorithm, the actual reordering and its inverse.

Author(s)

Havard Rue hrue@r-inla.org

Examples

g = system.file("demodata/germany.graph", package="INLA")
 r = inla.qreordering(g)
 m = inla.graph2matrix(g)
 r = inla.qreordering(m)

Generate samples from a GMRF using the GMRFLib implementation

Description

This function generate samples from a GMRF using the GMRFLib implementation

Usage

inla.qsample(
  n = 1L,
  Q,
  b,
  mu,
  sample,
  constr,
  reordering = INLA::inla.reorderings(),
  seed = 0L,
  logdens = ifelse(missing(sample), FALSE, TRUE),
  compute.mean = ifelse(missing(sample), FALSE, TRUE),
  num.threads = NULL,
  selection = NULL,
  verbose = inla.getOption("verbose"),
  .debug = FALSE
)

Arguments

n

Number of samples. Only used if missing(sample)
Q

The precision matrix or a filename containing it.
b

The linear term
mu

The mu term
sample

A matrix of optional samples where each column is a sample. If set, then evaluate the log-density for each sample only.
constr

Optional linear constraints; see ?INLA::f and argument extraconstr
reordering

The type of reordering algorithm to be used for TAUCS; either one of the names listed in inla.reorderings() or the output from inla.qreordering(Q). The default is "auto" which try several reordering algorithm and use the best one for this particular matrix.
seed

Control the RNG. If seed=0L then GMRFLib will set the seed intelligently/at 'random', and this is and should be the default behaviour. If seed < 0L then the saved state of the RNG will be reused if possible, otherwise, GMRFLib will set the seed intelligently/at 'random'. If seed > 0L then this value is used as the seed for the RNG.

PLEASE NOTE1: If seed!=0 then the computations will run in serial mode, over-riding whatever is set in num.threads (a warning might be issued).

PLEASE NOTE2: If the PARDISO sparse matrix library is used, continuity of the samples with respect to small changes in the precision matrix, can be expected but is not guaranteed. If this feature is required, please use the TAUCS sparse matrix library.
logdens

If TRUE, compute also the log-density of each sample. Note that the output format then change.
compute.mean

If TRUE, compute also the (constrained) mean. Note that the output format then change.
num.threads

Maximum number of threads the inla-program will use, or as 'A:B' defining the number threads in the outer (A) and inner (B) layer for nested parallelism. seed!=0 requires serial comptuations.
selection

A vector of indices of each sample to return. NULL means return the whole sample. (Note that the log-density retured, is for the whole sample.) The use of selection cannot be combined with the use of sample.
verbose

Logical. Run in verbose mode or not.
.debug

Logical. Internal debug-mode.

Value

The log-density has form -1/2(x-mu)^T Q (x-mu) + b^T x

If logdens is FALSE, then inla.qsample returns the samples in a matrix, where each column is a sample. If logdens or compute.mean is TRUE, then a list with names sample, logdens and mean is returned. The samples are stored in the matrix sample where each column is a sample, and the log densities of each sample are stored as the vector logdens. The mean (include corrections for the constraints, if any) is store in the vector mean.

Author(s)

Havard Rue hrue@r-inla.org

Examples

g = system.file("demodata/germany.graph", package="INLA")
 Q = inla.graph2matrix(g)
 diag(Q) = dim(Q)[1L]
 x = inla.qsample(10, Q)
 ## Not run: matplot(x)
 x = inla.qsample(10, Q, logdens=TRUE)
 ## Not run: matplot(x$sample)

 n = 3
 Q = diag(n)
 ns = 2

 ## sample and evaluate a sample
 x = inla.qsample(n, Q=Q, logdens=TRUE)
 xx = inla.qsample(Q=Q,  sample = x$sample)
 print(x$logdens - xx$logdens)

 ## the use of a constraint
 constr = list(A = matrix(rep(1, n), 1, n), e = 0)
 x = inla.qsample(n, Q=Q, constr=constr)
 print(constr$A %*% x)

 ## control the RNG (require serial mode)
 x = inla.qsample(n, Q=Q, seed = 123, num.threads="1:1")
 ## restart from same seed,  only sample 1
 xx = inla.qsample(n=1, Q=Q, seed = 123, num.threads="1:1")
 ## continue from the save state, sample the remaining 2
 xxx = inla.qsample(n=n-1, Q=Q, seed = -1, num.threads="1:1")
 ## should be 0
 print(x - cbind(xx, xxx))

Solves linear SPD systems

Description

This routine use the GMRFLib implementation to solve linear systems with a SPD matrix.

Usage

inla.qsolve(
  Q,
  B,
  reordering = inla.reorderings(),
  method = c("solve", "forward", "backward")
)

Arguments

Q

A SPD matrix, either as a (dense) matrix or sparse-matrix
B

The right hand side matrix, either as a (dense) matrix or sparse-matrix.
reordering

The type of reordering algorithm to be used for TAUCS; either one of the names listed in inla.reorderings() or the output from inla.qreordering(Q). The default is "auto" which try several reordering algorithm and use the best one for this particular matrix (using the TAUCS library).
method

The system to solve, one of "solve", "forward" or "backward". Let ⁠Q = L L^T⁠, where L is lower triangular (the Cholesky triangle), then method="solve" solves ⁠L L^T X = B⁠ or equivalently ⁠Q X = B⁠, method="forward" solves ⁠L X = B⁠, and method="backward" solves ⁠L^T X = B⁠.

Value

inla.qsolve returns a matrix X, which is the solution of ⁠Q X = B⁠, ⁠L X = B⁠ or ⁠L^T X = B⁠ depending on the value of method.

Author(s)

Havard Rue hrue@r-inla.org

Examples

n = 10
 nb <- n-1
 QQ = matrix(rnorm(n^2), n, n)
 QQ <- QQ %*% t(QQ)

 Q = inla.as.sparse(QQ)
 B = matrix(rnorm(n*nb), n, nb)

 X = inla.qsolve(Q, B, method = "solve")
 XX = inla.qsolve(Q, B, method = "solve", reordering = inla.qreordering(Q))
 print(paste("err solve1", sum(abs( Q %*% X - B))))
 print(paste("err solve2", sum(abs( Q %*% XX - B))))

 ## the forward and backward solve is tricky, as after permutation and with Q=LL', then L is
 ## lower triangular, but L in the orginal ordering is not lower triangular. if the rhs is iid
 ## noise, this is not important. to control the reordering, then the 'taucs' library must be
 ## used.
 inla.setOption(smtp = 'taucs')

 ## case 1. use the matrix as is, no reordering
 r <- "identity"
 L = t(chol(Q))
 X = inla.qsolve(Q, B, method = "forward", reordering = r)
 XX = inla.qsolve(Q, B, method = "backward", reordering = r)
 print(paste("err forward ", sum(abs(L %*% X - B))))
 print(paste("err backward", sum(abs(t(L) %*% XX - B))))

 ## case 2. use a reordering from the library
 r <- inla.qreordering(Q)
 im <- r$ireordering
 m <- r$reordering
 print(cbind(idx = 1:n, m, im) )
 Qr <- Q[im, im]
 L = t(chol(Qr))[m, m]

 X = inla.qsolve(Q, B, method = "forward", reordering = r)
 XX = inla.qsolve(Q, B, method = "backward", reordering = r)
 print(paste("err forward ", sum(abs( L %*% X - B))))
 print(paste("err backward", sum(abs( t(L) %*% XX - B))))

Read and write a graph-object

Description

Construct a graph-object from a file or a matrix; write graph-object to file

Usage

inla.read.graph(..., size.only = FALSE)

inla.write.graph(
  graph,
  filename = "graph.dat",
  mode = c("binary", "ascii"),
  ...
)

## S3 method for class 'inla.graph'
plot(x, y, ...)

## S3 method for class 'inla.graph'
summary(object, ...)

## S3 method for class 'inla.graph.summary'
print(x, ...)

Arguments

...

Additional arguments. In inla.read.graph, then it is the graph definition (object, matrix, character, filename), plus extra arguments. In inla.write.graph it is extra arguments to inla.read.graph.
size.only

Only read the size of the graph
graph

An inla.graph-object, a (sparse) symmetric matrix, a filename containing the graph, a list or collection of characters and/or numbers defining the graph, or a neighbours list with class nb (see spdep::card and spdep::poly2nb for for details of nb and an example a function returning an nb object
filename

The filename of the graph.
mode

The mode of the file; ascii-file or a (gzip-compressed) binary.
x

An inla.graph -object
y

Not used
object

An inla.graph -object

Value

The output of inla.read.graph, is an inla.graph object, with elements

n

is the size of the graph
nnbs

is a vector with the number of neigbours
nbs

is a list-list with the neigbours
cc

list with connected component information

  • idis a vector with the connected component id for each node (starting from 1)

  • nis the number of connected components

  • nodesis a list-list of nodes belonging to each connected component

  • meanis a factor with one level for each connected component of size larger than one, otherwise NA

Methods implemented for inla.graph are summary and plot. The method plot require the libraries Rgraphviz and graph from the Bioconductor-project, see https://www.bioconductor.org.

Author(s)

Havard Rue hrue@r-inla.org

See Also

inla.spy()

Examples

## a graph from a file
g.file1 <- tempfile() # E.g. "g.dat"
cat("3 1 1 2 2 1 1 3 0\n", file = g.file1)
g = inla.read.graph(g.file1)
## writing an inla.graph-object to file
g.file2 = inla.write.graph(g, mode="binary", filename = tempfile())
## re-reading it from that file
gg = inla.read.graph(g.file2)
summary(g)
summary(gg)

## Not run: 
plot(g)
inla.spy(g)
## when defining the graph directly in the call,
## we can use a mix of character and numbers
g = inla.read.graph(c(3, 1, "1 2 2 1 1 3", 0))
inla.spy(c(3, 1, "1 2 2 1 1 3 0"))
inla.spy(c(3, 1, "1 2 2 1 1 3 0"),  reordering=3:1)
inla.write.graph(c(3, 1, "1 2 2 1 1 3 0"))

## building a graph from adjacency matrix
adjacent = matrix(0, nrow = 4, ncol = 4)
adjacent[1,4] = adjacent[4,1] = 1
adjacent[2,4] = adjacent[4,2] = 1
adjacent[2,3] = adjacent[3,2] = 1
adjacent[3,4] = adjacent[4,3] = 1
g = inla.read.graph(adjacent)
plot(g)
summary(g)

## End(Not run)

rgeneric models

Description

A framework for defining latent models in R

Usage

inla.rgeneric.ar1.model(
  cmd = c("graph", "Q", "mu", "initial", "log.norm.const", "log.prior", "quit"),
  theta = NULL
)

inla.rgeneric.ar1.model.opt(
  cmd = c("graph", "Q", "mu", "initial", "log.norm.const", "log.prior", "quit"),
  theta = NULL
)

inla.rgeneric.iid.model(
  cmd = c("graph", "Q", "mu", "initial", "log.norm.const", "log.prior", "quit"),
  theta = NULL
)

inla.rgeneric.define(
  model = NULL,
  debug = FALSE,
  compile = TRUE,
  optimize = FALSE,
  ...
)

inla.rgeneric.wrapper(
  cmd = c("graph", "Q", "mu", "initial", "log.norm.const", "log.prior", "quit"),
  model,
  theta = NULL
)

inla.rgeneric.q(
  rmodel,
  cmd = c("graph", "Q", "mu", "initial", "log.norm.const", "log.prior", "quit"),
  theta = NULL
)

Arguments

cmd

An allowed request
theta

Values of theta
model

The definition of the model; see inla.rgeneric.ar1.model
debug

Logical. Enable debug output
compile

Logical. Compile the definition of the model or not.
optimize

Logical. With this option TRUE, then model pass only the values of Q and not the whole matrix. Please see the vignette for details and inla.rgeneric.ar1.model.opt for an example.
...

Named list of variables that defines the environment of model
rmodel

The rgeneric model-object, the output of inla.rgeneric.define

Value

This allows a latent model to be defined in R. See inla.rgeneric.ar1.model and inla.rgeneric.iid.model and the documentation for worked out examples of how to define latent models in this way. This will be somewhat slow and is intended for special cases and protyping. The function inla.rgeneric.wrapper is for internal use only.

Author(s)

Havard Rue hrue@r-inla.org

Define prior in R

Description

A(n experimental) framework for defining a prior in R

Usage

inla.rprior.define(rprior = NULL, ...)

Arguments

rprior

An R-function returning the log-prior evaluated at its argument
...

Named list of variables that will be in the environment of rprior

Value

An inla.rprior-object which can be used as a prior

Author(s)

Havard Rue hrue@r-inla.org

Examples

## see example in inla.doc("rprior")

Extra-Poisson variation in dose-response study

Description

Breslow (1984) analyses some mutagenicity assay data (shown below) on salmonella in which three plates have been processed at each dose i of quinoline and the number of revertant colonies of TA98 Salmonella measured

Format

A data frame with 18 observations on the following 3 variables.

y

number of salmonella bacteria

dose

dose of quinoline (mg per plate)

rand

indicator

Source

WinBUGS/OpenBUGS manual Examples VOl.I

Examples

data(Salm)

Scale an intrinsic GMRF model

Description

This function scales an intrinsic GMRF model so the geometric mean of the marginal variances is one

Usage

inla.scale.model.internal(Q, constr = NULL, eps = sqrt(.Machine$double.eps))

inla.scale.model(Q, constr = NULL, eps = sqrt(.Machine$double.eps))

Arguments

Q

A SPD matrix, either as a (dense) matrix or sparseMatrix
constr

Linear constraints spanning the null-space of Q; see ?INLA::f and argument extraconstr
eps

A small constant added to the diagonal of Q if constr

Value

inla.scale.model returns a sparseMatrix of type dgTMatrix scaled so the geometric mean of the marginal variances (of the possible non-singular part of Q) is one, for each connected component of the matrix.

Author(s)

Havard Rue hrue@r-inla.org

Examples

## Q is singular
 data(Germany)
 g = system.file("demodata/germany.graph", package="INLA")
 Q = -inla.graph2matrix(g)
 diag(Q) = 0
 diag(Q) = -rowSums(Q)
 n = dim(Q)[1]
 Q.scaled = inla.scale.model(Q, constr = list(A = matrix(1, 1, n), e=0))
 print(diag(MASS::ginv(as.matrix(Q.scaled))))

 ## Q is singular with 3 connected components
 g = inla.read.graph("6 1 2 2 3 2 2 1 3 3 2 1 2 4 1 5 5 1 4 6 0")
 print(paste("Number of connected components", g$cc$n))
 Q = -inla.graph2matrix(g)
 diag(Q) = 0
 diag(Q) = -rowSums(Q)
 n = dim(Q)[1]
 Q.scaled = inla.scale.model(Q, constr = list(A = matrix(1, 1, n), e=0))
 print(diag(MASS::ginv(as.matrix(Q.scaled))))

 ## Q is non-singular with 3 connected components. no constraints needed
 diag(Q) = diag(Q) + 1
 Q.scaled = inla.scale.model(Q)
 print(diag(MASS::ginv(as.matrix(Q.scaled))))

Get the parameterisation of the scopy model

Description

This function provide access to the parameterisation of the scopy model

Usage

inla.scopy.define(n = 5L)

Arguments

n

Number of parameters, either 2 (intercept and slope) or >= 5 (intercept, slope and a spline representing the deviation from it)

Value

A list with the number of parameters and matrix defining basis vectors for the scopy model

Author(s)

Havard Rue hrue@r-inla.org

Computes the mean and stdev for the spline from scopy

Description

This function computes the mean and stdev for the spline function that is implicite from an scopy model component

Usage

inla.scopy.summary(
  result,
  name,
  mean.value = NULL,
  slope.value = NULL,
  by = 0.01,
  range = c(0, 1),
  debug = FALSE
)

Arguments

result

An inla-object, ie the output from an inla() call
name

The name of the scopy model component see ?INLA::f and argument scopy
mean.value

In case where the mean of the spline is fixed and not estimated, you have to give it here
slope.value

In case where the slope of the spline is fixed and not estimated, you have to give it here
by

The resolution of the results, in the scale where diff(range(locations)) is 1
range

The range of the locations, as c(from, to)
debug

If TRUE then enable some debug output

Value

A data.frame with locations, mean and stdev. If name is not found, NULL is returned.

Author(s)

Havard Rue hrue@r-inla.org

Examples

## see example in inla.doc("scopy")

Conditional Autoregressive (CAR) model for disease mapping

Description

The rate of lip cancer in 56 counties in Scotland is recorder. The data set includes the observed and expected cases (based on the population and its age and sex distribution in the country), a covariate measuring the percentage of the population engaged in agricolture, fishing or forestry and the "position" of each county expressed as a list of adjacent counties

Format

A data frame with 56 observations on the following 4 variables.

Counts

The number of lip cancer registered

E

The expected number of lip cancer

X

The percentage of the population engaged in agricolture, fishing or forestry

Region

The county

Source

OpenBUGS Example manual, GeoBUGS

References

Clayton and Kaldor (1987) and Breslow and Clayton (1993)

Examples

data(Scotland)

Factorial design

Description

Proportion of seeds that germinated on each of 21 plates arranged according to a 2 by 2 factorial layout by seed and type of root extract

Format

A data frame with 21 observations on the following 5 variables.

r

number of germinated seeds per plate

n

number of total seeds per plate

x1

seed type

x2

root extracted

plate

indicator for the plate

Source

WinBUGS/OpenBUGS Manual Example, Vol. I

Examples

data(Seeds)

toy simulated data set for the SPDE tutorial

Description

Simulated data set on 200 location points. The simulation process is made at the introduction of the SPDE tutorial.

Format

A data frame with 200 observations on the following 3 variables.

s1

First element of the coordinates

s2

Second element of the coordinates

y

data simulated at the locations

Source

SPDE tutorial

Examples

data(SPDEtoy)

Summary for a INLA fit

Description

Takes a fitted inla or surv.inla object produced by inla or surv.inla and produces a summary from it.

Usage

## S3 method for class 'inla'
summary(object, digits = 3L, include.lincomb = TRUE, ...)

## S3 method for class 'summary.inla'
print(x, digits = 3L, ...)

Arguments

object

a fitted inla object as produced by inla.
digits

Integer Number of digits
include.lincomb

Logcial Include the summary for the the linear combinations or not
...

other arguments.
x

a summary.inla object produced by summary.inla

Details

Posterior mean and standard deviation (together with quantiles or cdf) are printed for the fixed effects in the model.

For the random effects the function summary() prints the posterior mean and standard deviations for the hyperparameters

If the option short.summary is set to TRUE using inla.setOption, then a less verbose summary variant will be used, which might be more suitable for Markdown documents.

Value

summary.inla returns an object of class summary.inla, a list of components to print.

Author(s)

Sara Martino and Havard Rue

See Also

inla()

Summarizing triangular mesh objects

Description

Construct and print inla.mesh object summaries

Usage

## S3 method for class 'inla.mesh'
summary(object, verbose = FALSE, ...)

## S3 method for class 'summary.inla.mesh'
print(x, ...)

Arguments

object

an object of class "inla.mesh", usually a result of a call to inla.mesh.create() or inla.mesh.2d().
verbose

If TRUE, produce a more detailed output.
...

further arguments passed to or from other methods.
x

an object of class "summary.inla.mesh", usually a result of a call to summary.inla.mesh().

Author(s)

Finn Lindgren finn.lindgren@gmail.com

Surgical: Institutional ranking

Description

This example considers mortality rates in 12 hospitals performing cardiac surgery in babies

Format

A data frame with 12 observations on the following 3 variables.

n

Number of deaths

r

Total number of cases

hospital

a factor with levels A B C D E F G H I J K L

Source

WinBUGS/OpenBUGS Manual Examples Vol. I

Examples

data(Surg)

Survival data

Description

Simulated data set for Weibull survival model

Format

A data frame with 100 observations on the following 3 variables.

y

a numeric vector of survival times

cens

a numeric vector of event indicator (0=censured 1=failure)

x

a numeric vector of covariate

Binomial time series

Description

Recorded days of rain above 1 mm in Tokyo for 2 years, 1983:84

Format

A data frame with 366 observations on the following 3 variables.

y

number of days with rain

n

total number of days

time

day of the year

Source

http://www.math.ntnu.no/~hrue/GMRF-book/tokyo.rainfall.data.dat

References

Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall

Examples

data(Tokyo)

Semiparametric regression

Description

Undernutrition of children in each region of Zambia is measured through a score computed on the basis of some anthropometric measures. The data set contains also other infomation about each child.

Format

A data frame with 4847 observations on the following 10 variables.

hazstd

standardised Z score of stunting

bmi

body mass index of the mother

agc

age of the child in months

district

district where the child lives

rcw

mother employment status with categories "working" (1) and "not working" (-1)

edu1

mother's educations status with categories "complete primary but incomplete secondary " (⁠edu1=1)⁠, "complete secondary or higher" (edu2=1) and "no education or incomplete primary" (edu1=edu2=-1)

edu2

see above

tpr

locality of the domicile with categories "urban" (1) and "rural" (-1)

sex

gender of the child with categories "male" (1) and "female" (-1)

edu

DO NOT KNOW; check source

Source

BayesX Manual http://www.stat.uni-muenchen.de/~bayesx/bayesx.html

Examples

data(Zambia)