R: Model Selection with Bayesian Methods and Information Criteria

# Model Selection with Bayesian Methods and Information Criteria

## Documentation for package ‘mombf’ version 3.5.4

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

A B C D E G H I L M N P Q R S U X Y Z

## -- A --

aic | Class "msPriorSpec"

## -- B --

bbPrior | Priors on model space for variable selection problems
bestAIC | Model with best AIC, BIC, EBIC or other general information criteria (getIC)
bestBIC | Model with best AIC, BIC, EBIC or other general information criteria (getIC)
bestEBIC | Model with best AIC, BIC, EBIC or other general information criteria (getIC)
bestIC | Model with best AIC, BIC, EBIC or other general information criteria (getIC)
bfnormmix | Number of Normal mixture components under Normal-IW and Non-local priors
bic | Class "msPriorSpec"
bicprior | Class "msPriorSpec"
binomPrior | Priors on model space for variable selection problems

## -- C --

cil | Treatment effect estimation for linear models via Confounder Importance Learning using non-local priors.
coef.mixturebf | Class "mixturebf"
coefByModel | Class "msfit"
coefByModel-method | Class "msfit"
coefByModel-methods | Class "msfit"

## -- D --

dalapl | Density and random draws from the asymmetric Laplace distribution
ddir | Dirichlet density
demom | Non-local prior density, cdf and quantile functions.
demom-method | Non-local prior density, cdf and quantile functions.
demom-methods | Non-local prior density, cdf and quantile functions.
demomigmarg | Non-local prior density, cdf and quantile functions.
dimom | Non-local prior density, cdf and quantile functions.
diwish | Density for Inverse Wishart distribution
dmom | Non-local prior density, cdf and quantile functions.
dmomigmarg | Non-local prior density, cdf and quantile functions.
dpostNIW | Posterior Normal-IWishart density

## -- E --

emomprior | Class "msPriorSpec"
eprod | Expectation of a product of powers of Normal or T random variables
exponentialprior | Class "msPriorSpec"

## -- G --

getAIC | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getAIC-method | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getAIC-methods | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getBIC | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getBIC-method | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getBIC-methods | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getEBIC | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getEBIC-method | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getEBIC-methods | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getIC | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getIC-method | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
getIC-methods | Obtain AIC, BIC, EBIC or other general information criteria (getIC)
groupemomprior | Class "msPriorSpec"
groupimomprior | Class "msPriorSpec"
groupmomprior | Class "msPriorSpec"
groupzellnerprior | Class "msPriorSpec"

## -- H --

hald | Hald Data

## -- I --

ic | Class "msPriorSpec"
icfit | Class "icfit"
icfit-class | Class "icfit"
icfit.coef | Class "icfit"
icfit.predict | Class "icfit"
icfit.summary | Class "icfit"
icov | Extract estimated inverse covariance
igprior | Class "msPriorSpec"
imombf | Moment and inverse moment Bayes factors for linear models.
imombf.lm | Moment and inverse moment Bayes factors for linear models.
imomknown | Bayes factors for moment and inverse moment priors
imomprior | Class "msPriorSpec"
imomunknown | Bayes factors for moment and inverse moment priors

## -- L --

localnulltest | Local variable selection
localnulltest_fda | Local variable selection
localnulltest_fda_givenknots | Local variable selection
localnulltest_givenknots | Local variable selection

## -- M --

marginalNIW | Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior.
marginalNIW-method | Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior.
marginalNIW-methods | Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior.
mixturebf | Class "mixturebf"
mixturebf-class | Class "mixturebf"
modelbbprior | Class "msPriorSpec"
modelbinomprior | Class "msPriorSpec"
modelcomplexprior | Class "msPriorSpec"
modelsearchBlockDiag | Bayesian variable selection for linear models via non-local priors.
modelSelection | Bayesian variable selection for linear models via non-local priors.
modelSelectionGGM | Bayesian variable selection for linear models via non-local priors.
modelunifprior | Class "msPriorSpec"
mombf | Moment and inverse moment Bayes factors for linear models.
mombf.lm | Moment and inverse moment Bayes factors for linear models.
momknown | Bayes factors for moment and inverse moment priors
momprior | Class "msPriorSpec"
momunknown | Bayes factors for moment and inverse moment priors
msfit | Class "msfit"
msfit-class | Class "msfit"
msfit.coef | Class "msfit"
msfit.plot | Class "msfit"
msfit.predict | Class "msfit"
msfit_ggm | Class "msfit_ggm"
msfit_ggm-class | Class "msfit_ggm"
msfit_ggm.coef | Class "msfit_ggm"
msPriorSpec | Class "msPriorSpec"
msPriorSpec-class | Class "msPriorSpec"

## -- N --

nlpMarginal | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors
nlpmarginals | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors
normalidprior | Class "msPriorSpec"

## -- P --

palapl | Density and random draws from the asymmetric Laplace distribution
pemom | Non-local prior density, cdf and quantile functions.
pemomigmarg | Non-local prior density, cdf and quantile functions.
pimom | Non-local prior density, cdf and quantile functions.
pimomMarginalK | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors
pimomMarginalU | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors
plotprior | Plot estimated marginal prior inclusion probabilities
plotprior-method | Plot estimated marginal prior inclusion probabilities
plotprior-methods | Plot estimated marginal prior inclusion probabilities
pmom | Non-local prior density, cdf and quantile functions.
pmomigmarg | Non-local prior density, cdf and quantile functions.
pmomMarginalK | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors
pmomMarginalU | Marginal density of the observed data for linear regression with Normal, two-piece Normal, Laplace or two-piece Laplace residuals under non-local and Zellner priors
postModeBlockDiag | Bayesian model selection and averaging under block-diagonal X'X for linear models.
postModeOrtho | Bayesian model selection and averaging under block-diagonal X'X for linear models.
postProb | Obtain posterior model probabilities
postProb-method | Obtain posterior model probabilities
postProb-methods | Obtain posterior model probabilities
postSamples | Extract posterior samples from an object
postSamples-method | Extract posterior samples from an object
postSamples-methods | Extract posterior samples from an object
priorp2g | Moment and inverse moment prior elicitation

## -- Q --

qimom | Non-local prior density, cdf and quantile functions.
qmom | Non-local prior density, cdf and quantile functions.

## -- R --

ralapl | Density and random draws from the asymmetric Laplace distribution
rnlp | Posterior sampling for regression parameters
rnlp-method | Posterior sampling for regression parameters
rnlp-methods | Posterior sampling for regression parameters
rpostNIW | Posterior Normal-IWishart density

## -- S --

show-method | Class "icfit"
show-method | Class "mixturebf"
show-method | Class "msfit"
show-method | Class "msfit_ggm"

## -- U --

unifPrior | Priors on model space for variable selection problems

## -- X --

x.hald | Hald Data

## -- Y --

y.hald | Hald Data

## -- Z --

zellnerprior | Class "msPriorSpec"