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:
mombf_3.5.4.tar.gz
mombf_3.5.4.tar.gz(r-4.5-noble)mombf_3.5.4.tar.gz(r-4.4-noble)
mombf_3.5.4.tgz(r-4.4-emscripten)mombf_3.5.4.tgz(r-4.3-emscripten)
mombf.pdf |mombf.html✨
mombf/json (API)
| # Install 'mombf' in R: |
| install.packages('mombf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/davidrusi/mombf/issues
Last updated 9 months agofrom:80d0db88db. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 04 2024 |
| R-4.5-linux-x86_64 | OK | Oct 04 2024 |
Exports:aicbbPriorbestAICbestBICbestEBICbestICbfnormmixbicbicpriorbinomPriorcilcoefByModeldalaplddirdemomdemomigmargdimomdiwishdmomdmomigmargdpostNIWemomprioreprodexponentialpriorgetAICgetBICgetEBICgetICgroupemompriorgroupimompriorgroupmompriorgroupzellnerprioricicovigpriorimombfimomknownimompriorimomunknownlocalnulltestlocalnulltest_fdalocalnulltest_fda_givenknotslocalnulltest_givenknotsmarginalNIWmodelbbpriormodelbinompriormodelcomplexpriormodelsearchBlockDiagmodelSelectionmodelSelectionGGMmodelunifpriormombfmomknownmompriormomunknownnlpMarginalnormalidpriorpalaplpemompemomigmargpimompimomMarginalKpimomMarginalUplotpriorpmompmomigmargpmomMarginalKpmomMarginalUpostModeBlockDiagpostModeOrthopostProbpostSamplespriorp2gqimomqmomralaplrnlprpostNIWunifPriorzellnerprior
Dependencies:clicodetoolsdplyrfansiforeachgenericsglassoglmnetglueintervalsiteratorslatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmclustmgcvmvtnormncvregnlmepillarpkgconfigpracmaR6RcppRcppArmadilloRcppEigenrlangshapesparseMatrixStatssurvivaltibbletidyselectutf8vctrswithr
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 |
