This package is considered a duplicate. The official version of this package is found at:https://dvats.r-universe.dev/mcmcse

Package: mcmcse 1.5-0

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. 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
mcmcse_1.5-0.tar.gz(r-4.5-noble)mcmcse_1.5-0.tar.gz(r-4.4-noble)
mcmcse_1.5-0.tgz(r-4.4-emscripten)mcmcse_1.5-0.tgz(r-4.3-emscripten)
mcmcse.pdf |mcmcse.html✨
mcmcse/json (API)

# Install 'mcmcse' in R:

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp

7.31 score 4 stars 17 packages 310 scripts 1.6k downloads 4 mentions 14 exports 28 dependencies

Last updated 3 years agofrom:c7bc2c7d2e. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 03 2024
R-4.5-linux-x86_64	NOTE	Dec 03 2024

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 digest ellipse evaluate fftwtools fs glue jsonlite lifecycle magrittr pkgbuild pkgload praise processx ps R6 Rcpp RcppArmadillo rlang rprojroot testthat waldo withr

Using mcmcse

Rendered frommcmcse_vignette.Rnwusingknitr::knitron Dec 03 2024.

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

Citation

Readme and manuals

Help Manual

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 Flgal (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