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., Gronau, Q. F., & Wagenmakers, E. (2017). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Manuscript Submitted for Publication.

[BibTeX]`@unpublished{heck2017quantifying, title = {Quantifying Uncertainty in Transdimensional {{Markov}} Chain {{Monte Carlo}} Using Discrete {{Markov}} Models}, archivePrefix = {arXiv}, eprinttype = {arxiv}, eprint = {1703.10364}, type = {Manuscript Submitted for Publication}, howpublished = {Manuscript submitted for publication}, author = {Heck, Daniel W and Overstall, Antony and Gronau, Quentin F and Wagenmakers, Eric-Jan}, date = {2017}, owner = {Daniel} }`