This package is considered a duplicate. The official version of this package is found at:https://davidrusi.r-universe.dev/mombf

Package: mombf 3.5.4

David Rossell

mombf: Model Selection with Bayesian Methods and Information Criteria

Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).

Authors:David Rossell, John D. Cook, Donatello Telesca, P. Roebuck, Oriol Abril, Miquel Torrens

mombf_3.5.4.tar.gz
mombf_3.5.4.zip(r-4.7-x86_64)mombf_3.5.4.zip(r-4.7-arm64)mombf_3.5.4.zip(r-4.6-x86_64)mombf_3.5.4.zip(r-4.6-arm64)mombf_3.5.4.zip(r-4.5-x86_64)
mombf_3.5.4.tgz(r-4.6-x86_64)mombf_3.5.4.tgz(r-4.6-arm64)mombf_3.5.4.tgz(r-4.5-x86_64)mombf_3.5.4.tgz(r-4.5-arm64)
mombf_3.5.4.tar.gz(r-4.7-arm64)mombf_3.5.4.tar.gz(r-4.7-x86_64)mombf_3.5.4.tar.gz(r-4.6-arm64)mombf_3.5.4.tar.gz(r-4.6-x86_64)
mombf_3.5.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mombf/json (API)

# Install 'mombf' in R:

install.packages('mombf', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davidrusi/mombf/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

hald - Hald Data
x.hald - Hald Data
y.hald - Hald Data

On CRAN:

archived packages r-universe openblas cpp

4.18 score 5 stars 2 packages 609 downloads 1 mentions 80 exports 37 dependencies

Last updated from:963f26329a (on package/mombf). Checks:15 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	212
linux-devel-x86_64	OK	244
source / vignettes	OK	373
linux-release-arm64	OK	207
linux-release-x86_64	OK	234
macos-release-arm64	OK	241
macos-release-x86_64	OK	399
macos-oldrel-arm64	OK	214
macos-oldrel-x86_64	OK	310
windows-devel-arm64	OK	326
windows-devel-x86_64	OK	311
windows-release-arm64	OK	320
windows-release-x86_64	OK	232
windows-oldrel-x86_64	OK	260
wasm-release	OK	169

Exports:aic bbPrior bestAIC bestBIC bestEBIC bestIC bfnormmix bic bicprior binomPrior cil coefByModel dalapl ddir demom demomigmarg dimom diwish dmom dmomigmarg dpostNIW emomprior eprod exponentialprior getAIC getBIC getEBIC getIC groupemomprior groupimomprior groupmomprior groupzellnerprior ic icov igprior imombf imomknown imomprior imomunknown localnulltest localnulltest_fda localnulltest_fda_givenknots localnulltest_givenknots marginalNIW modelbbprior modelbinomprior modelcomplexprior modelsearchBlockDiag modelSelection modelSelectionGGM modelunifprior mombf momknown momprior momunknown nlpMarginal normalidprior palapl pemom pemomigmarg pimom pimomMarginalK pimomMarginalU plotprior pmom pmomigmarg pmomMarginalK pmomMarginalU postModeBlockDiag postModeOrtho postProb postSamples priorp2g qimom qmom ralapl rnlp rpostNIW unifPrior zellnerprior

Dependencies:cli codetools dplyr foreach generics glasso glmnet glue intervals iterators lattice lifecycle magrittr Matrix MatrixGenerics matrixStats mclust mgcv mvtnorm ncvreg nlme pillar pkgconfig pracma R6 Rcpp RcppArmadillo RcppEigen rlang shape sparseMatrixStats survival tibble tidyselect utf8 vctrs withr

Manual for the mombf library

Rendered frommombf.Rnwusingutils::Sweave

Last update: 2026-09-30
Started: 2026-09-30

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Priors on model space for variable selection problems	bbPrior binomPrior unifPrior
Model with best AIC, BIC, EBIC or other general information criteria (getIC)	bestAIC bestBIC bestEBIC bestIC
Number of Normal mixture components under Normal-IW and Non-local priors	bfnormmix
Treatment effect estimation for linear models via Confounder Importance Learning using non-local priors.	cil
Density and random draws from the asymmetric Laplace distribution	dalapl palapl ralapl
Dirichlet density	ddir
Density for Inverse Wishart distribution	diwish
Non-local prior density, cdf and quantile functions.	demom demom,data.frame-method demom,matrix-method demom,vector-method demom-methods demomigmarg dimom dmom dmomigmarg pemom pemomigmarg pimom pmom pmomigmarg qimom qmom
Posterior Normal-IWishart density	dpostNIW rpostNIW
Expectation of a product of powers of Normal or T random variables	eprod
Obtain AIC, BIC, EBIC or other general information criteria (getIC)	getAIC getAIC,msfit-method getAIC-methods getBIC getBIC,msfit-method getBIC-methods getEBIC getEBIC,msfit-method getEBIC-methods getIC getIC,msfit-method getIC-methods
Hald Data	hald x.hald y.hald
Class "icfit"	icfit icfit-class icfit.coef icfit.predict icfit.summary show,icfit-method
Extract estimated inverse covariance	icov
Local variable selection	localnulltest localnulltest_fda localnulltest_fda_givenknots localnulltest_givenknots
Marginal likelihood under a multivariate Normal likelihood and a conjugate Normal-inverse Wishart prior.	marginalNIW marginalNIW,matrix,missing,missing,missing,missing-method marginalNIW,matrix,missing,missing,missing,vector-method marginalNIW,missing,ANY,matrix,numeric,missing-method marginalNIW,missing,list,list,numeric,missing-method marginalNIW-methods
Class "mixturebf"	coef.mixturebf mixturebf mixturebf-class show,mixturebf-method
Bayesian variable selection for linear models via non-local priors.	modelsearchBlockDiag modelSelection
Bayesian variable selection for linear models via non-local priors.	modelSelectionGGM
Moment and inverse moment Bayes factors for linear models.	imombf imombf.lm mombf mombf.lm
Bayes factors for moment and inverse moment priors	imomknown imomunknown momknown momunknown
Class "msfit_ggm"	msfit_ggm msfit_ggm-class msfit_ggm.coef show,msfit_ggm-method
Class "msfit"	coefByModel coefByModel,msfit-method coefByModel-methods msfit msfit-class msfit.coef msfit.plot msfit.predict show,msfit-method
Class "msPriorSpec"	aic bic bicprior emomprior exponentialprior groupemomprior groupimomprior groupmomprior groupzellnerprior ic igprior imomprior modelbbprior modelbinomprior modelcomplexprior modelunifprior momprior msPriorSpec msPriorSpec-class normalidprior zellnerprior
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	nlpMarginal nlpmarginals pimomMarginalK pimomMarginalU pmomMarginalK pmomMarginalU
Plot estimated marginal prior inclusion probabilities	plotprior plotprior,cilfit-method plotprior-methods
Bayesian model selection and averaging under block-diagonal X'X for linear models.	postModeBlockDiag postModeOrtho
Obtain posterior model probabilities	postProb postProb,cilfit-method postProb,localtest-method postProb,mixturebf-method postProb,msfit-method postProb-methods
Extract posterior samples from an object	postSamples postSamples,mixturebf-method postSamples-methods
Moment and inverse moment prior elicitation	priorp2g
Posterior sampling for regression parameters	rnlp rnlp,ANY,matrix,missing,missing,missing,character,character-method rnlp,ANY,matrix,missing,missing,missing,missing,missing-method rnlp,ANY,matrix,missing,missing,msfit,missing,missing-method rnlp,missing,missing,missing,missing,msfit,missing,missing-method rnlp,missing,missing,numeric,matrix,missing,missing,missing-method rnlp-methods