mcmcderive is an R package to generate derived parameter(s) from Monte
Carlo Markov Chain (MCMC) samples using R code. This is useful because
it means Bayesian models can be fitted without the inclusion of derived
parameters which add unnecessary clutter and slow model fitting. For
more information on MCMC samples see Brooks et al. (2011).
To install the latest release version from CRAN
To install the latest development version from GitHub
library(mcmcderive) mcmcr::mcmcr_example #> $alpha #>  3.718025 4.718025 #> #> nchains: 2 #> niters: 400 #> #> $beta #> [,1] [,2] #> [1,] 0.9716535 1.971654 #> [2,] 1.9716535 2.971654 #> #> nchains: 2 #> niters: 400 #> #> $sigma #>  0.7911975 #> #> nchains: 2 #> niters: 400 expr <- " log(alpha2) <- alpha gamma <- sum(alpha) * sigma " mcmc_derive(mcmcr::mcmcr_example, expr, silent = TRUE) #> $alpha2 #>  41.18352 111.94841 #> #> nchains: 2 #> niters: 400 #> #> $gamma #>  6.60742 #> #> nchains: 2 #> niters: 400
If the MCMC object has multiple chains the run time can be substantially reduced by generating the derived parameters for each chain in parallel. In order for this to work it is necessary to:
- Ensure plyr and doParallel are installed using
- Register a parallel backend using
parallel = TRUEin the call to
To facilitate the translation of model code into R code the
package also provides the R equivalent to common model functions such as
Please report any issues.
Pull requests are always welcome.
Please note that the ‘mcmcderive’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011. Handbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.