`mcmcderive`

?`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)

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
`install.packages(c("plyr", "doParallel"))`

. - Register a parallel backend using
`doParallel::registerDoParallel(4)`

. - Set
`parallel = TRUE`

in the call to`mcmc_derive()`

.

To facilitate the translation of model code into R code the `mcmcderive`

package also provides the R equivalent to common model functions such as `pow()`

, `phi()`

and `log() <-`

.

```
library(mcmcderive)
mcmcr::mcmcr_example
#> $alpha
#> [1] 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
#> [1] 0.7911975
#>
#> nchains: 2
#> niters: 400
expr <- "
log(alpha2) <- alpha
gamma <- sum(alpha) * sigma
"
mcmc_derive(mcmcr::mcmcr_example, expr, silent = TRUE)
#> $alpha2
#> [1] 41.18352 111.94841
#>
#> nchains: 2
#> niters: 400
#>
#> $gamma
#> [1] 6.60742
#>
#> nchains: 2
#> niters: 400
```

To install the latest development version from GitHub

`remotes::install_github("poissonconsulting/mcmcderive")`

To install the latest development version from the Poisson drat repository

```
drat::addRepo("poissonconsulting")
install.packages("mcmcderive")
```

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.