README.md

In danheck/MCMCprec: Precision of Discrete Parameters in Transdimensional MCMC

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 to 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 by typing the following into the console (required to compile the package RcppArmadillo):

curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

Reference

• Heck, D. W., Overstall, A. M., Gronau, Q. F., & Wagenmakers, E.-J. (2017). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics & Computing. doi:10.1007/s11222-018-9828-0 arxiv:1703.10364

danheck/MCMCprec documentation built on Nov. 13, 2022, 11:40 p.m.