CRAN Task View: Bayesian Inference

Maintainer:Jong Hee Park, Michela Cameletti, Xun Pang, Kevin M. Quinn
Contact:jongheepark at snu.ac.kr
Version:2022-04-06
URL:https://CRAN.R-project.org/view=Bayesian
Source:https://github.com/cran-task-views/Bayesian/
Contributions:Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.
Citation:Jong Hee Park, Michela Cameletti, Xun Pang, Kevin M. Quinn (2022). CRAN Task View: Bayesian Inference. Version 2022-04-06. URL https://CRAN.R-project.org/view=Bayesian.
Installation:The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("Bayesian", coreOnly = TRUE) installs all the core packages or ctv::update.views("Bayesian") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.

CRAN Task View: Bayesian Inference

Applied researchers interested in Bayesian statistics are increasingly attracted to R because of the ease of which one can code algorithms to sample from posterior distributions as well as the significant number of packages contributed to the Comprehensive R Archive Network (CRAN) that provide tools for Bayesian inference. This task view catalogs these tools. In this task view, we divide those packages into four groups based on the scope and focus of the packages. We first review R packages that provide Bayesian estimation tools for a wide range of models. We then discuss packages that address specific Bayesian models or specialized methods in Bayesian statistics. This is followed by a description of packages used for post-estimation analysis. Finally, we review packages that link R to other Bayesian sampling engines such as JAGS, OpenBUGS, WinBUGS, Stan, and TensorFlow.

General Purpose Model-Fitting Packages

Application-Specific Packages

ANOVA

Bayes factor/model comparison/Bayesian model averaging

Bayesian tree models

Causal inference

Computational methods

Discrete data

Experiment/Contingency table/meta analysis/AB testing methods

Graphics

Hierarchical models

High dimensional methods/machine learning methods

Factor analysis/item response theory models

Missing data

Mixture models

Network models/Matrix-variate distribution

Quantile regression

Shrinkage/Variable selection/Gaussian process

Spatial models

Survival models

Time series models

Other models

Bayesian models for specific disciplines

Post-estimation tools

Packages for learning Bayesian statistics

The Bayesian Inference Task View is written by Jong Hee Park (Seoul National University, South Korea), Andrew D. Martin (Washington University in St. Louis, MO, USA), and Kevin M. Quinn (UC Berkeley, Berkeley, CA, USA). Please e-mail the maintainer with suggestion or by submitting an issue or pull request in the GitHub repository linked above.

CRAN packages

Core:arm, BACCO, bayesforecast, bayesm, boa, coda, mcmc, MCMCpack, nimble.
Regular:abc, abcrf, abglasso, abtest, acebayes, AdMit, ammiBayes, AnaCoDa, AovBay, APFr, ArchaeoChron, ashr, autohd, BACCT, baggr, bain, BaM, bama, bamdit, bamlss, bang, BANOVA, BaPreStoPro, BART, bartBMA, bartCause, bartMachine, BAS, basad, basicMCMCplots, BaSkePro, BASS, baycn, bayefdr, bayes4psy, bayesAB, bayesammi, bayesanova, BayesARIMAX, BayesBinMix, bayesbio, bayesboot, BayesBP, bayesbr, BayesCACE, BayesCombo, BayesComm, bayescopulareg, bayescount, BayesCR, bayesCT, BayesCTDesign, BayesDA, bayesDccGarch, bayesdfa, bayesdistreg, bayesDP, BayesFactor, BayesFM, bayesGAM, bayesGARCH, BayesGOF, BayesGPfit, BayesGWQS, bayesian, bayesianETAS, BayesianFROC, Bayesiangammareg, BayesianGLasso, BayesianLaterality, BayesianNetwork, BayesianTools, bayesImageS, BayesLCA, bayesLife, bayeslincom, BayesLN, BayesLogit, bayesloglin, bayeslongitudinal, BayesMallows, BayesMassBal, bayesmeta, bayesmix, bayesQR, bayestestR, BayesTree, BayesVarSel, BayesX, BAYSTAR, bbricks, BCBCSF, BCE, bcp, BDgraph, Bergm, BEST, blavaan, BLR, BMA, Bmix, bmixture, BMS, bnlearn, BNSP, Bolstad, Boom, BoomSpikeSlab, bqtl, bridgesampling, brms, bsamGP, bspec, bspmma, bsts, BVAR, CARBayes, CARBayesdata, CARBayesST, causact, CausalImpact, CircSpaceTime, coalescentMCMC, coalitions, CPBayes, dbarts, dclone, deBInfer, densEstBayes, dfpk, dina, DIRECT, dirichletprocess, dlm, EbayesThresh, ebdbNet, edina, eigenmodel, ensembleBMA, EntropyMCMC, errum, FME, geoR, ggmcmc, gRain, greta, hbsae, HI, Hmisc, iterLap, LaplacesDemon, LAWBL, LearnBayes, lmm, loo, matchingMarkets, mcmcensemble, MCMCglmm, mcmcse, MCMCvis, mgcv, mlogitBMA, MNP, mombf, NetworkChange, NGSSEML, openEBGM, pcFactorStan, plotMCMC, PReMiuM, prevalence, pscl, R2jags, R2WinBUGS, ramps, Rbeast, REBayes, revdbayes, rjags, RoBMA, rrum, RSGHB, rstan, rstanarm, rstiefel, runjags, Runuran, RxCEcolInf, SamplerCompare, sbgcop, shinybrms, sna, spBayes, spikeslab, spikeSlabGAM, spTimer, ssgraph, ssMousetrack, stochvol, SuperLearner, tgp, vglmer, zic.

Related links

Other resources