|Maintainer:||Jong Hee Park, Michela Cameletti, Xun Pang, Kevin M. Quinn|
|Contact:||jongheepark at snu.ac.kr|
|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, |
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.
The arm package contains R functions for Bayesian inference using lm, glm, mer and polr objects.
BACCO is an R bundle for Bayesian analysis of random functions. BACCO contains three sub-packages: emulator, calibrator, and approximator, that perform Bayesian emulation and calibration of computer programs.
bayesforecast provides various functions for Bayesian time series analysis using ‘Stan’ for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series.
bayesm provides R functions for Bayesian inference for various models widely used in marketing and micro-econometrics. The models include linear regression models, multinomial logit, multinomial probit, multivariate probit, multivariate mixture of normals (including clustering), density estimation using finite mixtures of normals as well as Dirichlet Process priors, hierarchical linear models, hierarchical multinomial logit, hierarchical negative binomial regression models, and linear instrumental variable models.
BayesianTools is an R package for general-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
LaplacesDemon seeks to provide a complete Bayesian environment, including numerous MCMC algorithms, Laplace Approximation with multiple optimization algorithms, scores of examples, dozens of additional probability distributions, numerous MCMC diagnostics, Bayes factors, posterior predictive checks, a variety of plots, elicitation, parameter and variable importance, and numerous additional utility functions.
loo provides functions for efficient approximate leave-one-out cross-validation (LOO) for Bayesian models using Markov chain Monte Carlo. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, loo also provides standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.
MCMCpack provides model-specific Markov chain Monte Carlo (MCMC) algorithms for wide range of models commonly used in the social and behavioral sciences. It contains R functions to fit a number of regression models (linear regression, logit, ordinal probit, probit, Poisson regression, etc.), measurement models (item response theory and factor models), changepoint models (linear regression, binary probit, ordinal probit, Poisson, panel), and models for ecological inference. It also contains a generic Metropolis sampler that can be used to fit arbitrary models.
The mcmc package consists of an R function for a random-walk Metropolis algorithm for a continuous random vector.
The nimble package provides a general MCMC system that allows customizable MCMC for models written in the BUGS/JAGS model language. Users can choose samplers and write new samplers. Models and samplers are automatically compiled via generated C++. The package also supports other methods such as particle filtering or whatever users write in its algorithm language.
hitro.new()function in Runuran provides an MCMC sampler based on the Hit-and-Run algorithm in combination with the Ratio-of-Uniforms method.
bic.glm()of the BMA package that can be applied to multinomial logit (MNL) data.
krige.bayes()in the geoR package performs Bayesian analysis of geostatistical data allowing specification of different levels of uncertainty in the model parameters. See the Spatial view for more information.
gbayes()function in Hmisc derives the posterior (and optionally) the predictive distribution when both the prior and the likelihood are Gaussian, and when the statistic of interest comes from a two-sample problem.
vcov.gam()function the mgcv package can extract a Bayesian posterior covariance matrix of the parameters from a fitted
mcmcobject and related methods which are used by other packages. It can easily import MCMC output from WinBUGS, OpenBUGS, and JAGS, or from plain matrices. coda contains the Gelman and Rubin, Geweke, Heidelberger and Welch, and Raftery and Lewis diagnostics.
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.
|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.|