mcmcse v1.3-2

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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.

Functions in mcmcse

Name Description
mcse.mat Apply mcse to each column of a matrix or data frame of MCMC samples.
mcse.multi Multivariate Monte Carlo standard errors for expectations.
confRegion Confidence regions (ellipses) for Monte Carlo estimates
ess Estimate effective sample size (ESS) as described in Gong and Felgal (2015).
mcse.q Compute Monte Carlo standard errors for quantiles.
mcse.q.mat Apply mcse.q to each column of a matrix or data frame of MCMC samples.
estvssamp Create a plot that shows how Monte Carlo estimates change with increasing sample size.
mcmcse-package Monte Carlo Standard Errors for MCMC
mcse Compute Monte Carlo standard errors for expectations.
mcse.initseq Multivariate Monte Carlo standard errors for expectations with the initial sequence method of Dai and Jones (2017).
minESS Minimum effective sample size required for stable estimation as described in Vats et al. (2015).
multiESS Effective Sample Size of a multivariate Markov chain as described in Vats et al. (2015).
qqTest QQplot for Markov chains
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Vignettes of mcmcse

Name
mcmcse_vignette-concordance.tex
mcmcse_vignette.Rnw
mcse.bib
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Details

Date 2017-07-03
LinkingTo Rcpp, RcppArmadillo
License GPL (>= 2)
URL http://faculty.ucr.edu/~jflegal , http://johnhughes.org, http://www2.warwick.ac.uk/fac/sci/statistics/crism/visitors/vats
VignetteBuilder knitr
Repository CRAN
NeedsCompilation yes
Packaged 2017-07-04 07:02:42 UTC; Dootika
Date/Publication 2017-07-04 07:20:37 UTC

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