EntropyMCMC: MCMC Simulation and Convergence Evaluation using Entropy and Kullback-Leibler Divergence Estimation

Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.

Version: 1.0.4
Depends: R (≥ 3.0)
Imports: RANN, parallel, mixtools
Suggests: Rmpi, snow
Published: 2019-03-08
Author: Didier Chauveau [aut, cre], Houssam Alrachid [ctb]
Maintainer: Didier Chauveau <didier.chauveau at univ-orleans.fr>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: EntropyMCMC citation info
In views: Bayesian
CRAN checks: EntropyMCMC results


Reference manual: EntropyMCMC.pdf
Package source: EntropyMCMC_1.0.4.tar.gz
Windows binaries: r-devel: EntropyMCMC_1.0.4.zip, r-devel-UCRT: EntropyMCMC_1.0.4.zip, r-release: EntropyMCMC_1.0.4.zip, r-oldrel: EntropyMCMC_1.0.4.zip
macOS binaries: r-release (arm64): EntropyMCMC_1.0.4.tgz, r-release (x86_64): EntropyMCMC_1.0.4.tgz, r-oldrel: EntropyMCMC_1.0.4.tgz


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