Package: MCMCpack
Version: 1.6-3
Date: 2022-04-13
Title: Markov Chain Monte Carlo (MCMC) Package
Author: Andrew D. Martin [aut], Kevin M. Quinn [aut], Jong Hee Park [aut,cre], Ghislain Vieilledent [ctb], Michael Malecki[ctb], Matthew Blackwell [ctb], Keith Poole [ctb], Craig Reed [ctb], Ben Goodrich [ctb], Qiushi Yu [ctb], Ross Ihaka [cph], The R Development Core Team [cph], The R Foundation [cph], Pierre L'Ecuyer [cph], Makoto Matsumoto [cph], Takuji Nishimura [cph]
Maintainer: Jong Hee Park <jongheepark@snu.ac.kr>
Depends: R (>= 3.6), coda (>= 0.11-3), MASS, stats
Imports: graphics, grDevices, lattice, methods, utils, mcmc, quantreg
Description: Contains functions to perform Bayesian
inference using posterior simulation for a number of
statistical models. Most simulation is done in compiled C++
written in the Scythe Statistical Library Version 1.0.3. All
models return 'coda' mcmc objects that can then be summarized
using the 'coda' package. Some useful
utility functions such as density functions,
pseudo-random number generators for statistical
distributions, a general purpose Metropolis sampling algorithm,
and tools for visualization are provided.
License: GPL-3
SystemRequirements: gcc (>= 4.0)
URL: https://CRAN.R-project.org/package=MCMCpack
Packaged: 2022-04-13 10:14:59 UTC; park
NeedsCompilation: yes
Repository: CRAN
RoxygenNote: 7.1.2
Encoding: UTF-8
Date/Publication: 2022-04-13 11:12:32 UTC

Title: Precision of Discrete Parameters in Transdimensional MCMC
Maintainer: Daniel W. Heck <heck@uni-mannheim.de>
Description: Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017) <https://arxiv.org/abs/1703.10364> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.
Imports: Rcpp, parallel, utils, stats, Matrix, combinat
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppEigen