Package: mcmcse 1.5-1

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
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mcmcse_1.5-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mcmcse/json (API)

# Install 'mcmcse' in R:

install.packages('mcmcse', repos = c('https://dvats.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:

effective-sample-size mcmc output-a openblas cpp

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

Last updated from:6f0048066d. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	173
linux-devel-x86_64	NOTE	165
source / vignettes	OK	262
linux-release-arm64	NOTE	161
linux-release-x86_64	NOTE	156
macos-release-arm64	NOTE	156
macos-release-x86_64	NOTE	241
macos-oldrel-arm64	NOTE	178
macos-oldrel-x86_64	NOTE	510
windows-devel	NOTE	184
windows-release	NOTE	154
windows-oldrel	NOTE	165
wasm-release	OK	156

Exports:batchSize BVN_Gibbs confRegion ess estvssamp is.mcmcse mcse mcse.initseq mcse.mat mcse.multi mcse.q mcse.q.mat minESS multiESS

Dependencies:brio callr cli crayon desc diffobj ellipse evaluate fftwtools fs glue jsonlite lifecycle magrittr otel pkgbuild pkgload praise processx ps R6 Rcpp RcppArmadillo rlang rprojroot testthat waldo withr

Using mcmcse

Rendered frommcmcse_vignette.Rnwusingknitr::knitr

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

Help page	Topics
Monte Carlo Standard Errors for MCMC	mcmcse-package mcmcse
Batch size (truncation point) selection	batchSize
MCMC samples from a bivariate normal distribution	BVN_Gibbs
Confidence regions (ellipses) for Monte Carlo estimates	confRegion
Univariate effective sample size (ESS) as described in Gong and Flegal (2015)	ess
Create a plot that shows how Monte Carlo estimates change with increasing sample size	estvssamp
Check if the class of the object is mcmcse	is.mcmcse
Compute Monte Carlo standard errors for expectations	mcse
Multivariate Monte Carlo standard errors for expectations with the initial sequence method of Dai and Jones (2017)	mcse.initseq
Apply 'mcse' to each column of the MCMC samples	mcse.mat
Multivariate Monte Carlo standard errors for expectations	mcse.multi
Compute Monte Carlo standard errors for quantiles	mcse.q
Apply 'mcse.q' to each column of a matrix or data frame of MCMC samples	mcse.q.mat
Minimum effective sample size required for stable estimation as described in Vats et al. (2015)	minESS
Effective Sample Size of a multivariate Markov chain as described in Vats et al. (2015)	multiESS

Package: mcmcse 1.5-1

mcmcse: Monte Carlo Standard Errors for MCMC

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Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)