Package: mcmcse 1.5-0

mcmcse: Monte Carlo Standard Errors for MCMC

Provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. 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 <[email protected]>, John Hughes, Dootika Vats <[email protected]>, Ning Dai, Kushagra Gupta, and Uttiya Maji

mcmcse_1.5-0.tar.gz
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mcmcse_1.5-0.tgz(r-4.4-x86_64)mcmcse_1.5-0.tgz(r-4.4-arm64)mcmcse_1.5-0.tgz(r-4.3-x86_64)mcmcse_1.5-0.tgz(r-4.3-arm64)
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mcmcse.pdf |mcmcse.html✨
mcmcse/json (API)

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

Peer review:

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

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

On CRAN:

effective-sample-sizemcmcoutput-a

8.74 score 12 stars 17 packages 302 scripts 1.5k downloads 4 mentions 14 exports 28 dependencies

Last updated 3 years agofrom:154c4ba447. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64NOTENov 06 2024
R-4.5-linux-x86_64NOTENov 06 2024
R-4.4-win-x86_64NOTENov 06 2024
R-4.4-mac-x86_64NOTENov 06 2024
R-4.4-mac-aarch64NOTENov 06 2024
R-4.3-win-x86_64NOTENov 06 2024
R-4.3-mac-x86_64NOTENov 06 2024
R-4.3-mac-aarch64NOTENov 06 2024

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

Dependencies:briocallrclicrayondescdiffobjdigestellipseevaluatefftwtoolsfsgluejsonlitelifecyclemagrittrpkgbuildpkgloadpraiseprocessxpsR6RcppRcppArmadillorlangrprojroottestthatwaldowithr

Using mcmcse

Rendered frommcmcse_vignette.Rnwusingknitr::knitron Nov 06 2024.

Last update: 2021-08-19
Started: 2018-09-03