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mcmcse

An R package 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.

Installation

This R package is on CRAN, and its preferred URL being https://CRAN.R-project.org/package=mcmcse.

To download this development repo, through the the devtools package:

# install.packages("devtools")
library(devtools)
devtools::install_github("dvats/mcmcse")

Citation

Please run citation("mcmcse") after loading the package for citation details.

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Version

Install

install.packages('mcmcse')

Monthly Downloads

1,435

Version

1.4-1

License

GPL (>= 2)

Homepage

http://faculty.ucr.edu/~jflegal

Maintainer

Dootika Vats

Last Published

January 29th, 2020

Functions in mcmcse (1.4-1)

Apply mcse to each column of a matrix or data frame of MCMC samples.

Create a plot that shows how Monte Carlo estimates change with increasing sample size.

QQplot for Markov chains

Univariate estimate effective sample size (ESS) as described in Gong and Felgal (2015).

Effective Sample Size of a multivariate Markov chain as described in Vats et al. (2015).

Batch size (truncation point) selection

Confidence regions (ellipses) for Monte Carlo estimates

Minimum effective sample size required for stable estimation as described in Vats et al. (2015).

Apply mcse.q to each column of a matrix or data frame of MCMC samples.

Compute Monte Carlo standard errors for quantiles.

Multivariate Monte Carlo standard errors for expectations.

Multivariate Monte Carlo standard errors for expectations with the initial sequence method of Dai and Jones (2017).

Compute Monte Carlo standard errors for expectations.

Monte Carlo Standard Errors for MCMC