models: Valid models in INLA
In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Description Usage Value Examples

This page describe the models implemented in inla, divided into sections: latent, group, mix, link, predictor, hazard, likelihood, prior, wrapper .

1	inla.models()

Valid sections are: latent, group, mix, link, predictor, hazard, likelihood, prior, wrapper

Section ‘latent’.

Valid models in this section are:

Model ‘linear’.

Number of hyperparmeters are 0.

Model ‘iid’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘mec’.

Number of hyperparmeters are 4.

Hyperparameter ‘theta1’

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’

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’

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’

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)'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
status =: ‘experimental’
pdf =: ‘mec’

Model ‘meb’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
status =: ‘experimental’
pdf =: ‘meb’

Model ‘rgeneric’.

Number of hyperparmeters are 0.

Model ‘rw1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

constr =: ‘TRUE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
pdf =: ‘rw1’

Model ‘rw2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

constr =: ‘TRUE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
pdf =: ‘rw2’

Model ‘crw2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

constr =: ‘TRUE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘2’
aug.constr =: ‘1’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
pdf =: ‘crw2’

Model ‘seasonal’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘besag’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘besag2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

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 =: ‘besag2’

Model ‘bym’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

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’

Model ‘bym2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

name =: ‘logit phi’
short.name =: ‘phi’
prior =: ‘pc’
param =: ‘0.5 -1’
initial =: ‘-3’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x/(1-x))'
from.theta =: 'function(x) exp(x)/(1+exp(x))'

Properties:

constr =: ‘TRUE’
nrow.ncol =: ‘FALSE’
augmented =: ‘TRUE’
aug.factor =: ‘2’
aug.constr =: ‘2’
n.div.by =: ‘NULL’
n.required =: ‘TRUE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘bym2’

Model ‘besagproper’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘TRUE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘besagproper’

Model ‘besagproper2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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))'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘TRUE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘besagproper2’

Model ‘ar1’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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'

Properties:

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’

Model ‘ar’.

Number of hyperparmeters are 11.

Hyperparameter ‘theta1’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘pc.prec’
param =: ‘1 0.01’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘pacf1’
short.name =: ‘pacf1’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘pc.ar’
param =: ‘1’
to.theta =: 'function(x) log((1+x)/(1-x))'
from.theta =: 'function(x) 2*exp(x)/(1+exp(x))-1'

Hyperparameter ‘theta3’

name =: ‘pacf2’
short.name =: ‘pacf2’
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 ‘theta4’

name =: ‘pacf3’
short.name =: ‘pacf3’
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’

name =: ‘pacf4’
short.name =: ‘pacf4’
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’

name =: ‘pacf5’
short.name =: ‘pacf5’
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’

name =: ‘pacf6’
short.name =: ‘pacf6’
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’

name =: ‘pacf7’
short.name =: ‘pacf7’
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’

name =: ‘pacf8’
short.name =: ‘pacf8’
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’

name =: ‘pacf9’
short.name =: ‘pacf9’
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’

name =: ‘pacf10’
short.name =: ‘pacf10’
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'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
status =: ‘experimental’
pdf =: ‘ar’

Model ‘ou’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

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’

Model ‘generic’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘generic0’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘generic1’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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))'

Properties:

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’

Model ‘generic2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

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’

Model ‘generic3’.

Number of hyperparmeters are 11.

Hyperparameter ‘theta1’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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)'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘TRUE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘generic3’

Model ‘spde’.

Number of hyperparmeters are 4.

Hyperparameter ‘theta1’

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’

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’

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’

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))'

Properties:

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’

Model ‘spde2’.

Number of hyperparmeters are 100.

Hyperparameter ‘theta1’

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’

name =: ‘theta2’
short.name =: ‘t2’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta3’

name =: ‘theta3’
short.name =: ‘t3’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta4’

name =: ‘theta4’
short.name =: ‘t4’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

name =: ‘theta5’
short.name =: ‘t5’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

name =: ‘theta6’
short.name =: ‘t6’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

name =: ‘theta7’
short.name =: ‘t7’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

name =: ‘theta8’
short.name =: ‘t8’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

name =: ‘theta9’
short.name =: ‘t9’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

name =: ‘theta10’
short.name =: ‘t10’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

name =: ‘theta11’
short.name =: ‘t11’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta12’

name =: ‘theta12’
short.name =: ‘t12’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta13’

name =: ‘theta13’
short.name =: ‘t13’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta14’

name =: ‘theta14’
short.name =: ‘t14’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta15’

name =: ‘theta15’
short.name =: ‘t15’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta16’

name =: ‘theta16’
short.name =: ‘t16’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta17’

name =: ‘theta17’
short.name =: ‘t17’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta18’

name =: ‘theta18’
short.name =: ‘t18’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta19’

name =: ‘theta19’
short.name =: ‘t19’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta20’

name =: ‘theta20’
short.name =: ‘t20’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta21’

name =: ‘theta21’
short.name =: ‘t21’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta22’

name =: ‘theta22’
short.name =: ‘t22’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta23’

name =: ‘theta23’
short.name =: ‘t23’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta24’

name =: ‘theta24’
short.name =: ‘t24’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta25’

name =: ‘theta25’
short.name =: ‘t25’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta26’

name =: ‘theta26’
short.name =: ‘t26’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta27’

name =: ‘theta27’
short.name =: ‘t27’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta28’

name =: ‘theta28’
short.name =: ‘t28’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta29’

name =: ‘theta29’
short.name =: ‘t29’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta30’

name =: ‘theta30’
short.name =: ‘t30’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta31’

name =: ‘theta31’
short.name =: ‘t31’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta32’

name =: ‘theta32’
short.name =: ‘t32’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta33’

name =: ‘theta33’
short.name =: ‘t33’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta34’

name =: ‘theta34’
short.name =: ‘t34’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta35’

name =: ‘theta35’
short.name =: ‘t35’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta36’

name =: ‘theta36’
short.name =: ‘t36’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta37’

name =: ‘theta37’
short.name =: ‘t37’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta38’

name =: ‘theta38’
short.name =: ‘t38’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta39’

name =: ‘theta39’
short.name =: ‘t39’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta40’

name =: ‘theta40’
short.name =: ‘t40’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta41’

name =: ‘theta41’
short.name =: ‘t41’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta42’

name =: ‘theta42’
short.name =: ‘t42’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta43’

name =: ‘theta43’
short.name =: ‘t43’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta44’

name =: ‘theta44’
short.name =: ‘t44’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta45’

name =: ‘theta45’
short.name =: ‘t45’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta46’

name =: ‘theta46’
short.name =: ‘t46’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta47’

name =: ‘theta47’
short.name =: ‘t47’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta48’

name =: ‘theta48’
short.name =: ‘t48’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta49’

name =: ‘theta49’
short.name =: ‘t49’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta50’

name =: ‘theta50’
short.name =: ‘t50’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta51’

name =: ‘theta51’
short.name =: ‘t51’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta52’

name =: ‘theta52’
short.name =: ‘t52’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta53’

name =: ‘theta53’
short.name =: ‘t53’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta54’

name =: ‘theta54’
short.name =: ‘t54’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta55’

name =: ‘theta55’
short.name =: ‘t55’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta56’

name =: ‘theta56’
short.name =: ‘t56’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta57’

name =: ‘theta57’
short.name =: ‘t57’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta58’

name =: ‘theta58’
short.name =: ‘t58’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta59’

name =: ‘theta59’
short.name =: ‘t59’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta60’

name =: ‘theta60’
short.name =: ‘t60’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta61’

name =: ‘theta61’
short.name =: ‘t61’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta62’

name =: ‘theta62’
short.name =: ‘t62’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta63’

name =: ‘theta63’
short.name =: ‘t63’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta64’

name =: ‘theta64’
short.name =: ‘t64’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta65’

name =: ‘theta65’
short.name =: ‘t65’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta66’

name =: ‘theta66’
short.name =: ‘t66’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta67’

name =: ‘theta67’
short.name =: ‘t67’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta68’

name =: ‘theta68’
short.name =: ‘t68’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta69’

name =: ‘theta69’
short.name =: ‘t69’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta70’

name =: ‘theta70’
short.name =: ‘t70’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta71’

name =: ‘theta71’
short.name =: ‘t71’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta72’

name =: ‘theta72’
short.name =: ‘t72’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta73’

name =: ‘theta73’
short.name =: ‘t73’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta74’

name =: ‘theta74’
short.name =: ‘t74’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta75’

name =: ‘theta75’
short.name =: ‘t75’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta76’

name =: ‘theta76’
short.name =: ‘t76’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta77’

name =: ‘theta77’
short.name =: ‘t77’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta78’

name =: ‘theta78’
short.name =: ‘t78’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta79’

name =: ‘theta79’
short.name =: ‘t79’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta80’

name =: ‘theta80’
short.name =: ‘t80’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta81’

name =: ‘theta81’
short.name =: ‘t81’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta82’

name =: ‘theta82’
short.name =: ‘t82’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta83’

name =: ‘theta83’
short.name =: ‘t83’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta84’

name =: ‘theta84’
short.name =: ‘t84’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta85’

name =: ‘theta85’
short.name =: ‘t85’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta86’

name =: ‘theta86’
short.name =: ‘t86’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta87’

name =: ‘theta87’
short.name =: ‘t87’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta88’

name =: ‘theta88’
short.name =: ‘t88’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta89’

name =: ‘theta89’
short.name =: ‘t89’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta90’

name =: ‘theta90’
short.name =: ‘t90’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta91’

name =: ‘theta91’
short.name =: ‘t91’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta92’

name =: ‘theta92’
short.name =: ‘t92’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta93’

name =: ‘theta93’
short.name =: ‘t93’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta94’

name =: ‘theta94’
short.name =: ‘t94’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta95’

name =: ‘theta95’
short.name =: ‘t95’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta96’

name =: ‘theta96’
short.name =: ‘t96’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta97’

name =: ‘theta97’
short.name =: ‘t97’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta98’

name =: ‘theta98’
short.name =: ‘t98’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta99’

name =: ‘theta99’
short.name =: ‘t99’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta100’

name =: ‘theta100’
short.name =: ‘t100’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

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’

Model ‘spde3’.

Number of hyperparmeters are 100.

Hyperparameter ‘theta1’

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’

name =: ‘theta2’
short.name =: ‘t2’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta3’

name =: ‘theta3’
short.name =: ‘t3’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta4’

name =: ‘theta4’
short.name =: ‘t4’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

name =: ‘theta5’
short.name =: ‘t5’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

name =: ‘theta6’
short.name =: ‘t6’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

name =: ‘theta7’
short.name =: ‘t7’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

name =: ‘theta8’
short.name =: ‘t8’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

name =: ‘theta9’
short.name =: ‘t9’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

name =: ‘theta10’
short.name =: ‘t10’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

name =: ‘theta11’
short.name =: ‘t11’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta12’

name =: ‘theta12’
short.name =: ‘t12’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta13’

name =: ‘theta13’
short.name =: ‘t13’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta14’

name =: ‘theta14’
short.name =: ‘t14’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta15’

name =: ‘theta15’
short.name =: ‘t15’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta16’

name =: ‘theta16’
short.name =: ‘t16’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta17’

name =: ‘theta17’
short.name =: ‘t17’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta18’

name =: ‘theta18’
short.name =: ‘t18’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta19’

name =: ‘theta19’
short.name =: ‘t19’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta20’

name =: ‘theta20’
short.name =: ‘t20’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta21’

name =: ‘theta21’
short.name =: ‘t21’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta22’

name =: ‘theta22’
short.name =: ‘t22’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta23’

name =: ‘theta23’
short.name =: ‘t23’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta24’

name =: ‘theta24’
short.name =: ‘t24’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta25’

name =: ‘theta25’
short.name =: ‘t25’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta26’

name =: ‘theta26’
short.name =: ‘t26’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta27’

name =: ‘theta27’
short.name =: ‘t27’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta28’

name =: ‘theta28’
short.name =: ‘t28’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta29’

name =: ‘theta29’
short.name =: ‘t29’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta30’

name =: ‘theta30’
short.name =: ‘t30’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta31’

name =: ‘theta31’
short.name =: ‘t31’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta32’

name =: ‘theta32’
short.name =: ‘t32’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta33’

name =: ‘theta33’
short.name =: ‘t33’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta34’

name =: ‘theta34’
short.name =: ‘t34’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta35’

name =: ‘theta35’
short.name =: ‘t35’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta36’

name =: ‘theta36’
short.name =: ‘t36’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta37’

name =: ‘theta37’
short.name =: ‘t37’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta38’

name =: ‘theta38’
short.name =: ‘t38’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta39’

name =: ‘theta39’
short.name =: ‘t39’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta40’

name =: ‘theta40’
short.name =: ‘t40’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta41’

name =: ‘theta41’
short.name =: ‘t41’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta42’

name =: ‘theta42’
short.name =: ‘t42’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta43’

name =: ‘theta43’
short.name =: ‘t43’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta44’

name =: ‘theta44’
short.name =: ‘t44’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta45’

name =: ‘theta45’
short.name =: ‘t45’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta46’

name =: ‘theta46’
short.name =: ‘t46’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta47’

name =: ‘theta47’
short.name =: ‘t47’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta48’

name =: ‘theta48’
short.name =: ‘t48’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta49’

name =: ‘theta49’
short.name =: ‘t49’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta50’

name =: ‘theta50’
short.name =: ‘t50’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta51’

name =: ‘theta51’
short.name =: ‘t51’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta52’

name =: ‘theta52’
short.name =: ‘t52’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta53’

name =: ‘theta53’
short.name =: ‘t53’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta54’

name =: ‘theta54’
short.name =: ‘t54’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta55’

name =: ‘theta55’
short.name =: ‘t55’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta56’

name =: ‘theta56’
short.name =: ‘t56’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta57’

name =: ‘theta57’
short.name =: ‘t57’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta58’

name =: ‘theta58’
short.name =: ‘t58’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta59’

name =: ‘theta59’
short.name =: ‘t59’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta60’

name =: ‘theta60’
short.name =: ‘t60’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta61’

name =: ‘theta61’
short.name =: ‘t61’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta62’

name =: ‘theta62’
short.name =: ‘t62’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta63’

name =: ‘theta63’
short.name =: ‘t63’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta64’

name =: ‘theta64’
short.name =: ‘t64’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta65’

name =: ‘theta65’
short.name =: ‘t65’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta66’

name =: ‘theta66’
short.name =: ‘t66’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta67’

name =: ‘theta67’
short.name =: ‘t67’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta68’

name =: ‘theta68’
short.name =: ‘t68’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta69’

name =: ‘theta69’
short.name =: ‘t69’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta70’

name =: ‘theta70’
short.name =: ‘t70’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta71’

name =: ‘theta71’
short.name =: ‘t71’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta72’

name =: ‘theta72’
short.name =: ‘t72’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta73’

name =: ‘theta73’
short.name =: ‘t73’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta74’

name =: ‘theta74’
short.name =: ‘t74’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta75’

name =: ‘theta75’
short.name =: ‘t75’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta76’

name =: ‘theta76’
short.name =: ‘t76’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta77’

name =: ‘theta77’
short.name =: ‘t77’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta78’

name =: ‘theta78’
short.name =: ‘t78’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta79’

name =: ‘theta79’
short.name =: ‘t79’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta80’

name =: ‘theta80’
short.name =: ‘t80’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta81’

name =: ‘theta81’
short.name =: ‘t81’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta82’

name =: ‘theta82’
short.name =: ‘t82’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta83’

name =: ‘theta83’
short.name =: ‘t83’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta84’

name =: ‘theta84’
short.name =: ‘t84’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta85’

name =: ‘theta85’
short.name =: ‘t85’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta86’

name =: ‘theta86’
short.name =: ‘t86’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta87’

name =: ‘theta87’
short.name =: ‘t87’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta88’

name =: ‘theta88’
short.name =: ‘t88’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta89’

name =: ‘theta89’
short.name =: ‘t89’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta90’

name =: ‘theta90’
short.name =: ‘t90’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta91’

name =: ‘theta91’
short.name =: ‘t91’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta92’

name =: ‘theta92’
short.name =: ‘t92’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta93’

name =: ‘theta93’
short.name =: ‘t93’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta94’

name =: ‘theta94’
short.name =: ‘t94’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta95’

name =: ‘theta95’
short.name =: ‘t95’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta96’

name =: ‘theta96’
short.name =: ‘t96’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta97’

name =: ‘theta97’
short.name =: ‘t97’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta98’

name =: ‘theta98’
short.name =: ‘t98’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta99’

name =: ‘theta99’
short.name =: ‘t99’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta100’

name =: ‘theta100’
short.name =: ‘t100’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

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’

Model ‘iid1d’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘iid2d’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

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’

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’

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'

Properties:

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’

Model ‘iid3d’.

Number of hyperparmeters are 6.

Hyperparameter ‘theta1’

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’

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’

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’

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’

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’

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'

Properties:

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’

Model ‘iid4d’.

Number of hyperparmeters are 10.

Hyperparameter ‘theta1’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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'

Properties:

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’

Model ‘iid5d’.

Number of hyperparmeters are 15.

Hyperparameter ‘theta1’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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’

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'

Properties:

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’

Model ‘2diid’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

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’

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’

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'

Properties:

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’

Model ‘z’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’
status =: ‘experimental’

Model ‘rw2d’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

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’

Model ‘rw2diid’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

name =: ‘logit phi’
short.name =: ‘phi’
prior =: ‘pc’
param =: ‘0.5 -1’
initial =: ‘3’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x/(1-x))'
from.theta =: 'function(x) exp(x)/(1+exp(x))'

Properties:

constr =: ‘TRUE’
nrow.ncol =: ‘TRUE’
augmented =: ‘TRUE’
aug.factor =: ‘2’
aug.constr =: ‘2’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘TRUE’
status =: ‘experimental’
pdf =: ‘rw2diid’

Model ‘slm’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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))'

Properties:

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’
status =: ‘experimental’

Model ‘matern2d’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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)'

Properties:

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’

Model ‘copy’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘beta’
short.name =: ‘b’
initial =: ‘1’
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") '

Properties:

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’

Model ‘clinear’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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") '

Properties:

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’

Model ‘sigm’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

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’

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’

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)'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
status =: ‘experimental’
pdf =: ‘sigm’

Model ‘revsigm’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

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’

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’

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)'

Properties:

constr =: ‘FALSE’
nrow.ncol =: ‘FALSE’
augmented =: ‘FALSE’
aug.factor =: ‘1’
aug.constr =: ‘NULL’
n.div.by =: ‘NULL’
n.required =: ‘FALSE’
set.default.values =: ‘FALSE’
status =: ‘experimental’
pdf =: ‘sigm’

Section ‘group’.

Valid models in this section are:

Model ‘exchangeable’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Model ‘ar1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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'

Properties:

Model ‘ar’.

Number of hyperparmeters are 11.

Hyperparameter ‘theta1’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘0’
fixed =: ‘TRUE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘pacf1’
short.name =: ‘pacf1’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘mvnorm’
param =: ‘0 0.15’
to.theta =: 'function(x) log((1+x)/(1-x))'
from.theta =: 'function(x) 2*exp(x)/(1+exp(x))-1'

Hyperparameter ‘theta3’

name =: ‘pacf2’
short.name =: ‘pacf2’
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 ‘theta4’

name =: ‘pacf3’
short.name =: ‘pacf3’
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’

name =: ‘pacf4’
short.name =: ‘pacf4’
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’

name =: ‘pacf5’
short.name =: ‘pacf5’
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’

name =: ‘pacf6’
short.name =: ‘pacf6’
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’

name =: ‘pacf7’
short.name =: ‘pacf7’
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’

name =: ‘pacf8’
short.name =: ‘pacf8’
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’

name =: ‘pacf9’
short.name =: ‘pacf9’
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’

name =: ‘pacf10’
short.name =: ‘pacf10’
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'

Properties:

Model ‘rw1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Model ‘rw2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Model ‘besag’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Model ‘I’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Section ‘mix’.

Valid models in this section are:

Model ‘gaussian’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision’
short.name =: ‘prec’
prior =: ‘loggamma’
param =: ‘1 0.01’
initial =: ‘0’
fixed =: ‘FALSE’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

Section ‘link’.

Valid models in this section are:

Model ‘default’.

Number of hyperparmeters are 0.

Model ‘cloglog’.

Number of hyperparmeters are 0.

Model ‘identity’.

Number of hyperparmeters are 0.

Model ‘log’.

Number of hyperparmeters are 0.

Model ‘logit’.

Number of hyperparmeters are 0.

Model ‘probit’.

Number of hyperparmeters are 0.

Model ‘tan’.

Number of hyperparmeters are 0.

Model ‘sslogit’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

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))'

Properties:

pdf =: ‘NA’

Model ‘logoffset’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

pdf =: ‘logoffset’

Model ‘test1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘beta’
short.name =: ‘b’
prior =: ‘normal’
param =: ‘0 100’
initial =: ‘0’
fixed =: ‘FALSE’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

pdf =: ‘NA’

Model ‘special2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘beta’
short.name =: ‘b’
prior =: ‘normal’
param =: ‘0 10’
initial =: ‘0’
fixed =: ‘FALSE’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

pdf =: ‘NA’

Model ‘special1’.

Number of hyperparmeters are 11.

Hyperparameter ‘theta1’

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’

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’

name =: ‘beta2’
short.name =: ‘beta2’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta4’

name =: ‘beta3’
short.name =: ‘beta3’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta5’

name =: ‘beta4’
short.name =: ‘beta4’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta6’

name =: ‘beta5’
short.name =: ‘beta5’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta7’

name =: ‘beta6’
short.name =: ‘beta6’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta8’

name =: ‘beta7’
short.name =: ‘beta7’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta9’

name =: ‘beta8’
short.name =: ‘beta8’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta10’

name =: ‘beta9’
short.name =: ‘beta9’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta11’

name =: ‘beta10’
short.name =: ‘beta10’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘none’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

pdf =: ‘NA’

Section ‘predictor’.

Valid models in this section are:

Model ‘predictor’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘11’
fixed =: ‘TRUE’
prior =: ‘loggamma’
param =: ‘1 1e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

Section ‘hazard’.

Valid models in this section are:

Model ‘rw1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Model ‘rw2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

Section ‘likelihood’.

Valid models in this section are:

Model ‘poisson’.

Number of hyperparmeters are 0.

Model ‘gpoisson’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘overdispersion’
short.name =: ‘phi’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘p’
short.name =: ‘p’
initial =: ‘1’
fixed =: ‘TRUE’
prior =: ‘normal’
param =: ‘1 100’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default log logoffset’
pdf =: ‘gpoisson’
status =: ‘experimental’

Model ‘binomial’.

Number of hyperparmeters are 0.

Model ‘testbinomial1’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘sensitivity’
short.name =: ‘s’
initial =: ‘3’
fixed =: ‘FALSE’
prior =: ‘logitbeta’
param =: ‘2 1’
to.theta =: 'function(x) log(x/(1-x))'
from.theta =: 'function(x) exp(x)/(1+exp(x))'

Hyperparameter ‘theta2’

name =: ‘specificity’
short.name =: ‘e’
initial =: ‘3’
fixed =: ‘FALSE’
prior =: ‘logitbeta’
param =: ‘2 1’
to.theta =: 'function(x) log(x/(1-x))'
from.theta =: 'function(x) exp(x)/(1+exp(x))'

Properties:

status =: ‘experimental’
survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default logit probit cloglog log’
pdf =: ‘testbinomial1’

Model ‘gamma’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘precision parameter’
short.name =: ‘prec’
initial =: ‘4.60517018598809’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.01’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘gamma’

Model ‘gammacount’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log alpha’
short.name =: ‘alpha’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘10 10’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
status =: ‘experimental’
pdf =: ‘gammacount’

Model ‘kumar’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘precision parameter’
short.name =: ‘prec’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.001’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘quantile’
short.name =: ‘q’
initial =: ‘0.5’
fixed =: ‘TRUE’
prior =: ‘invalid’
param =: ''
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit’
pdf =: ‘kumar’

Model ‘beta’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘precision parameter’
short.name =: ‘phi’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘beta’

Model ‘betabinomial’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘overdispersion’
short.name =: ‘rho’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default logit probit cloglog’
pdf =: ‘betabinomial’

Model ‘cbinomial’.

Number of hyperparmeters are 0.

Model ‘nbinomial’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘size’
short.name =: ‘size’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default log logoffset’
pdf =: ‘nbinomial’

Model ‘simplex’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘simplex’

Model ‘gaussian’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity logit log’
pdf =: ‘gaussian’

Model ‘normal’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘gaussian’

Model ‘circularnormal’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision parameter’
short.name =: ‘prec’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.01’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default tan’
pdf =: ‘circular-normal’
status =: ‘experimental’

Model ‘wrappedcauchy’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision parameter’
short.name =: ‘prec’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default tan’
pdf =: ‘wrapped-cauchy’
status =: ‘disabled’

Model ‘iidgamma’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘logshape’
short.name =: ‘shape’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘100 100’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘lograte’
short.name =: ‘rate’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘100 100’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘iidgamma’
status =: ‘experimental’

Model ‘iidlogitbeta’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log.a’
short.name =: ‘a’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘log.b’
short.name =: ‘b’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit’
pdf =: ‘iidlogitbeta’
status =: ‘experimental’

Model ‘loggammafrailty’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘loggammafrailty’

Model ‘logistic’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘logistic’

Model ‘skewnormal’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘inverse.scale’
short.name =: ‘iscale’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’

Hyperparameter ‘theta2’

name =: ‘skewness’
short.name =: ‘skew’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0 10’

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘sn’

Model ‘sn’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log inverse scale’
short.name =: ‘iscale’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’

Hyperparameter ‘theta2’

name =: ‘logit skewness’
short.name =: ‘skew’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0 10’
to.theta =: 'function(x, shape.max = 1) log((1+x/shape.max)/(1-x/shape.max))'
from.theta =: 'function(x, shape.max = 1) shape.max*(2*exp(x)/(1+exp(x))-1)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘sn’

Model ‘sn2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’

Hyperparameter ‘theta2’

name =: ‘logit skewness’
short.name =: ‘skew’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0 10’
to.theta =: 'function(x) log((1+x)/(1-x))'
from.theta =: 'function(x) (2*exp(x)/(1+exp(x))-1)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
status =: ‘experimental’
pdf =: ‘sn2’

Model ‘gev’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

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’

name =: ‘gev parameter’
short.name =: ‘gev’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0 25’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
status =: ‘experimental’
pdf =: ‘gev’

Model ‘laplace’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

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)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
status =: ‘disabled’
pdf =: ‘laplace’

Model ‘lognormal’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘TRUE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘lognormal’

Model ‘exponential’.

Number of hyperparmeters are 0.

Model ‘coxph’.

Number of hyperparmeters are 0.

Model ‘weibull’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log alpha’
short.name =: ‘a’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘25 25’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘TRUE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘weibull’

Model ‘loglogistic’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log alpha’
short.name =: ‘alpha’
initial =: ‘1’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘25 25’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘TRUE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘loglogistic’

Model ‘weibullcure’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log alpha’
short.name =: ‘a’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘25 25’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘TRUE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘NA’

Model ‘stochvol’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘500’
fixed =: ‘TRUE’
prior =: ‘loggamma’
param =: ‘1 0.005’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘stochvolgaussian’

Model ‘stochvolt’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log degrees of freedom’
short.name =: ‘dof’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.5’
to.theta =: 'function(x) log(x-2)'
from.theta =: 'function(x) 2+exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘stochvolt’

Model ‘stochvolnig’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘skewness’
short.name =: ‘skew’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0 10’
to.theta =: 'function(x) x'
from.theta =: 'function(x) x'

Hyperparameter ‘theta2’

name =: ‘shape’
short.name =: ‘shape’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.5’
to.theta =: 'function(x) log(x-1)'
from.theta =: 'function(x) 1+exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘stochvolnig’

Model ‘zeroinflatedpoisson0’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedpoisson1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedpoisson2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log alpha’
short.name =: ‘a’
initial =: ‘0.693147180559945’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0.693147180559945 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedbetabinomial0’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘overdispersion’
short.name =: ‘rho’
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’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default logit probit cloglog’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedbetabinomial1’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘overdispersion’
short.name =: ‘rho’
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’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘TRUE’
link =: ‘default logit probit cloglog’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedbinomial0’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedbinomial1’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘zeroinflated’

Model ‘zeroinflatedbinomial2’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘alpha’
short.name =: ‘alpha’
initial =: ‘-1’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘-1 0.2’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘zeroinflated’

Model ‘zeroninflatedbinomial2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘alpha1’
short.name =: ‘alpha1’
initial =: ‘-1’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘-1 0.2’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘alpha2’
short.name =: ‘alpha2’
initial =: ‘-1’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘-1 0.2’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘NA’

Model ‘zeroinflatedbetabinomial2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log alpha’
short.name =: ‘a’
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’

name =: ‘beta’
short.name =: ‘b’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘0 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default logit probit cloglog’
pdf =: ‘zeroinflated’

Model ‘zeroinflatednbinomial0’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log size’
short.name =: ‘size’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatednbinomial1’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log size’
short.name =: ‘size’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatednbinomial1strata2’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

name =: ‘log size’
short.name =: ‘size’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘logit probability 1’
short.name =: ‘prob1’
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’

name =: ‘logit probability 2’
short.name =: ‘prob2’
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))'

Properties:

status =: ‘experimental’
survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatednbinomial1strata3’.

Number of hyperparmeters are 3.

Hyperparameter ‘theta1’

name =: ‘log size 1’
short.name =: ‘size1’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘log size 2’
short.name =: ‘size2’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta3’

name =: ‘logit probability’
short.name =: ‘prob’
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))'

Properties:

status =: ‘experimental’
survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘zeroinflatednbinomial2’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log size’
short.name =: ‘size’
initial =: ‘2.30258509299405’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘log alpha’
short.name =: ‘a’
initial =: ‘0.693147180559945’
fixed =: ‘FALSE’
prior =: ‘gaussian’
param =: ‘2 1’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default log’
pdf =: ‘zeroinflated’

Model ‘t’.

Number of hyperparmeters are 2.

Hyperparameter ‘theta1’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘0’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta2’

name =: ‘log degrees of freedom’
short.name =: ‘dof’
initial =: ‘5’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.5’
to.theta =: 'function(x) log(x-2)'
from.theta =: 'function(x) 2+exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘student-t’

Model ‘tstrata’.

Number of hyperparmeters are 11.

Hyperparameter ‘theta1’

name =: ‘log degrees of freedom’
short.name =: ‘dof’
initial =: ‘4’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 0.01’
to.theta =: 'function(x) log(x-5)'
from.theta =: 'function(x) 5+exp(x)'

Hyperparameter ‘theta2’

name =: ‘log precision1’
short.name =: ‘prec1’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta3’

name =: ‘log precision2’
short.name =: ‘prec2’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta4’

name =: ‘log precision3’
short.name =: ‘prec3’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta5’

name =: ‘log precision4’
short.name =: ‘prec4’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta6’

name =: ‘log precision5’
short.name =: ‘prec5’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta7’

name =: ‘log precision6’
short.name =: ‘prec6’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta8’

name =: ‘log precision7’
short.name =: ‘prec7’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta9’

name =: ‘log precision8’
short.name =: ‘prec8’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta10’

name =: ‘log precision9’
short.name =: ‘prec9’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Hyperparameter ‘theta11’

name =: ‘log precision10’
short.name =: ‘prec10’
initial =: ‘2’
fixed =: ‘FALSE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

survival =: ‘FALSE’
discrete =: ‘FALSE’
link =: ‘default identity’
pdf =: ‘tstrata’

Model ‘logperiodogram’.

Number of hyperparmeters are 0.

Section ‘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 ‘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 ‘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.ar’.: 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.rho0’.: Number of parameters in the prior = 2
Model ‘pc.rho1’.: Number of parameters in the prior = 2
Model ‘pc.spde.GA’.: Number of parameters in the prior = 4
Model ‘pc’.: Number of parameters in the prior = 2
Model ‘ref.ar’.: Number of parameters in the prior = 0
Model ‘jeffreystdf’.: Number of parameters in the prior = 0
Model ‘expression:’.: Number of parameters in the prior = -1
Model ‘table:’.: Number of parameters in the prior = -1

Section ‘wrapper’.

Valid models in this section are:

Model ‘joint’.

Number of hyperparmeters are 1.

Hyperparameter ‘theta’

name =: ‘log precision’
short.name =: ‘prec’
initial =: ‘0’
fixed =: ‘TRUE’
prior =: ‘loggamma’
param =: ‘1 5e-05’
to.theta =: 'function(x) log(x)'
from.theta =: 'function(x) exp(x)'

Properties:

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’

## 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' take preceedence
## 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)))

andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.

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andrewzm/INLA
Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

models: Valid models in INLA
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andrewzm/INLA Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

models: Valid models in INLA In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

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andrewzm/INLA
Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

models: Valid models in INLA
In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion