fmcmc: A friendly MCMC framework
Journal of Open Source Software ยท 2019
Markov Chain Monte Carlo (MCMC) is used in a variety of statistical and computational venues such as: statistical inference, Markov quadrature (also known as Monte Carlo integration), stochastic optimization, among others.
Published2019Cited by8
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Abstract
Markov Chain Monte Carlo (MCMC) is used in a variety of statistical and computational venues such as: statistical inference, Markov quadrature (also known as Monte Carlo integration), stochastic optimization, among others. The fmcmc R package provides a flexible framework for implementing MCMC methods that use the Metropolis-Hastings algorithm. fmcmc provides the following out-of-the-box features that can be valuable for both practitioners of MCMC and educators: seamless efficient multiple-chain sampling using parallel computing, user-defined transition kernels, and automatic stop using convergence monitoring.
Cite
@article{VegaYon2019a,
title = {fmcmc: A friendly MCMC framework},
author = {{Vega Yon}, {George G.} and {Marjoram}, {Paul}},
year = {2019},
month = {7},
journal = {Journal of Open Source Software},
volume = {4},
number = {39},
pages = {1427},
doi = {10.21105/joss.01427},
url = {http://joss.theoj.org/papers/10.21105/joss.01427},
issn = {2475โ9066},
}