MCMCprecision: Precision for discrete parameters in transdimensional MCMC

The R package `MCMCprecision` estimates the precision of the posterior model probabilities in transdimensional Markov chain Monte Carlo methods (e.g., reversible jump MCMC or product-space MCMC). This is useful for applications of transdimensional MCMC, such as model selection, mixtures with varying numbers of components, change-point detection, capture-recapture models, phylogenetic trees, variable selection, and for discrete parameters in MCMC output in general.

To install MCMCprecision from GitHub, paste the following code into R (dependencies need to be installed manually):

### Dependencies:
# install.packages(c("combinat", "devtools", "RcppProgress", "RcppArmadillo", "RcppEigen"))

library(devtools)
install_github("danheck/MCMCprecision")

To compile C++ code, Windows requires Rtools and Mac Xcode Command Line Tools, respectively. Moreover, on Mac, it might be necessary to install the library gfortran manually, as required to compile the package RcppArmadillo.

Reference

• Heck, D. W., Overstall, A. M., Gronau, Q. F., & Wagenmakers, E.-J. (2019). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics & Computing, 29, 631–643. doi:10.1007/s11222-018-9828-0 arxiv:1703.10364