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MCMCprecision  

Precision of Discrete Parameters in Transdimensional MCMC
View on CRAN: Click here


Download and install MCMCprecision package within the R console
Install from CRAN:
install.packages("MCMCprecision")

Install from Github:
library("remotes")
install_github("cran/MCMCprecision")

Install by package version:
library("remotes")
install_version("MCMCprecision", "0.4.2")



Attach the package and use:
library("MCMCprecision")
Maintained by
Daniel W. Heck
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-04-03
Latest Update: 2025-07-22
Description:
Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.
How to cite:
Daniel W. Heck (2017). MCMCprecision: Precision of Discrete Parameters in Transdimensional MCMC. R package version 0.4.2, https://cran.r-project.org/web/packages/MCMCprecision. Accessed 09 Oct. 2026.
Previous versions and publish date:
0.3.6 (2017-04-03 08:17), 0.3.7 (2017-08-04 15:48), 0.3.8 (2018-04-08 19:11), 0.3.9 (2018-08-10 15:40), 0.4.0 (2019-12-05 10:00), (2026-07-09 08:09)
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Complete documentation for MCMCprecision
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