Package: mcmcse 1.5-1

Dootika Vats

mcmcse: Monte Carlo Standard Errors for MCMC

Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings (survey in <doi:10.1201/b10905>, Chapter 7). MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.

Authors:James M. Flegal [aut], John Hughes [aut], Dootika Vats [aut, cre], Ning Dai [aut], Kushagra Gupta [aut], Uttiya Maji [aut]

mcmcse_1.5-1.tar.gz
mcmcse_1.5-1.tar.gz(r-4.7-arm64)mcmcse_1.5-1.tar.gz(r-4.7-x86_64)mcmcse_1.5-1.tar.gz(r-4.6-arm64)mcmcse_1.5-1.tar.gz(r-4.6-x86_64)
mcmcse_1.5-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mcmcse/json (API)

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

Bug tracker:https://github.com/dvats/mcmcse/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

7.80 score 4 stars 12 packages 512 scripts 4.3k downloads 4 mentions 14 exports 28 dependencies

Last updated from:c61d9e45ff. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK163
linux-devel-x86_64OK166
source / vignettesOK247
linux-release-arm64OK162
linux-release-x86_64OK158
wasm-releaseOK146

Exports:batchSizeBVN_GibbsconfRegionessestvssampis.mcmcsemcsemcse.initseqmcse.matmcse.multimcse.qmcse.q.matminESSmultiESS

Dependencies:briocallrclicrayondescdiffobjellipseevaluatefftwtoolsfsgluejsonlitelifecyclemagrittrotelpkgbuildpkgloadpraiseprocessxpsR6RcppRcppArmadillorlangrprojroottestthatwaldowithr

Using mcmcse

Rendered frommcmcse_vignette.Rnwusingknitr::knitron Jun 18 2026.

Last update: 2021-09-09
Started: 2015-08-01