Package: fmcmc 0.6-0

George Vega Yon

fmcmc: A friendly MCMC framework

Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

Authors:George Vega Yon [aut, cre], Paul Marjoram [ctb, ths], National Cancer Institute [fnd], Fabian Scheipl [rev]

fmcmc_0.6-0.tar.gz
fmcmc_0.6-0.zip(r-4.7)fmcmc_0.6-0.zip(r-4.6)fmcmc_0.6-0.zip(r-4.5)
fmcmc_0.6-0.tgz(r-4.6-any)fmcmc_0.6-0.tgz(r-4.5-any)
fmcmc_0.6-0.tar.gz(r-4.7-any)fmcmc_0.6-0.tar.gz(r-4.6-any)
fmcmc_0.6-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
fmcmc/json (API)

# Install 'fmcmc' in R:
install.packages('fmcmc', repos = c('https://uscbiostats.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uscbiostats/fmcmc/issues

Pkgdown/docs site:https://uscbiostats.github.io

Datasets:

On CRAN:

Conda:

adaptivebayesian-inferencemarkov-chain-monte-carlomcmcmetropolis-hastingsparallel-computing

7.24 score 16 stars 1 packages 61 scripts 260 downloads 57 exports 4 dependencies

Last updated from:d7330a7d89. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK148
source / vignettesOK237
linux-release-x86_64OK150
macos-release-arm64OK151
macos-oldrel-arm64OK121
windows-develOK243
windows-releaseOK133
windows-oldrelOK125
wasm-releaseOK106

Exports:add_userdataappend_chainscheck_initialconvergence_autoconvergence_data_getconvergence_data_setconvergence_gelmanconvergence_gewekeconvergence_heildelconvergence_msg_getconvergence_msg_setcov_recursiveget_get_burninget_chain_idget_clget_conv_checkerget_drawsget_elapsedget_funget_initialget_kernelget_logpostget_multicoreget_nchainsget_nstepsget_progressget_seedget_thinget_userdataith_stepkernel_adaptkernel_amkernel_newkernel_nmirrorkernel_normalkernel_normal_reflectivekernel_ramkernel_umirrorkernel_unifkernel_unif_reflectivelast_LAST_CONV_CHECKlast_conv_checkerlast_elapsedlast_kernelLAST_MCMClast_nchainslast_nstepsMCMCMCMC_OUTPUTMCMC_without_conv_checkermean_recursivenew_progress_barplan_update_sequencereflect_on_boundariesset_userdata

Dependencies:codalatticeMASSMatrix

Advanced features
Life expectancy in the US | Operating within the loop | Using ith_step() with multiple chains | Saving information as it happens

Last update: 2025-12-09
Started: 2021-07-20

Workflow with fmcmc
Motivating example | Reusing fmcmc_kernel objects | Accessing other elements of the chain

Last update: 2021-07-21
Started: 2019-08-13

User-defined kernels
Introduction | Size parameter in a binomial distribution

Last update: 2021-07-20
Started: 2019-08-13

Readme and manuals

Help Manual

Help pageTopics
Append MCMC chains (objects of class coda::mcmc)append_chains
Checks the initial values of the MCMCcheck_initial
Convergence Monitoringautomatic-stop convergence-checker convergence_auto convergence_data_get convergence_data_set convergence_gelman convergence_geweke convergence_heildel convergence_msg_get convergence_msg_set LAST_CONV_CHECK
Recursive algorithms for computing variance and meancov_recursive mean_recursive
A friendly MCMC frameworkfmcmc-package fmcmc
Deprecated methods in fmcmcfmcmc-deprecated last_ last_conv_checker last_elapsed last_kernel LAST_MCMC last_nchains last_nsteps
Adaptive Metropolis (AM) Transition Kernelkernel_adapt kernel_am
Mirror Transition Kernelskernel_mirror kernel_nmirror kernel_umirror
Transition Kernels for MCMCfmcmc_kernel kernels kernel_new
Gaussian Transition Kernelkernel_normal kernel_normal_reflective
Robust Adaptive Metropolis (RAM) Transition Kernelkernel_ram
Uniform Transition Kernelkernel_unif kernel_unif_reflective
Life expectancy in the US (2020)lifeexpect
Markov Chain Monte CarloMCMC MCMC_OUTPUT MCMC_without_conv_checker Metropolis-Hastings
Functions to interact with the main loopadd_userdata get_userdata ith_step mcmc-loop set_userdata
Information about the last 'MCMC' callget_ get_burnin get_chain_id get_cl get_conv_checker get_draws get_elapsed get_fun get_initial get_kernel get_logpost get_multicore get_nchains get_nsteps get_progress get_seed get_thin mcmc-output
Progress barnew_progress_bar
Parameters' update sequenceplan_update_sequence
Reflective Boundariesreflect_on_boundaries