This R package provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the ‘differential.evolution’ sampler from ter Braak and Vrugt (2008) and the ‘stretch’ sampler from Goodman and Weare (2010).

For theoretical background about Ensemble MCMC (what are the benefits over simple MCMC? How do they work? What are the pitfalls?), please refer for example to this lecture from Eric B. Ford (Penn State).

## Installation

You can install the stable version of this package from CRAN:

install.packages("mcmcensemble")

or the development version from GitHub:

# install.packages("remotes")
remotes::install_github("Bisaloo/mcmcensemble")

## Usage

library(mcmcensemble)

## a log-pdf to sample from
p.log <- function(x) {
B <- 0.03                              # controls 'bananacity'
-x[1]^2/200 - 1/2*(x[2]+B*x[1]^2-100*B)^2
}

## use stretch move
res1 <- MCMCEnsemble(p.log, lower.inits=c(a=0, b=0), upper.inits=c(a=1, b=1),
max.iter=3000, n.walkers=10, method="stretch")
#> Using stretch move with 10 walkers.
str(res1)
#> List of 2
#>  $samples: num [1:10, 1:300, 1:2] 0.14 0.665 0.995 0.653 0.476 ... #> ..- attr(*, "dimnames")=List of 3 #> .. ..$ : chr [1:10] "walker_1" "walker_2" "walker_3" "walker_4" ...
#>   .. ..$: chr [1:300] "generation_1" "generation_2" "generation_3" "generation_4" ... #> .. ..$ : chr [1:2] "a" "b"
#>  $log.p : num [1:10, 1:300] -3.59 -3.48 -2.41 -3.44 -2.44 ... #> ..- attr(*, "dimnames")=List of 2 #> .. ..$ : chr [1:10] "walker_1" "walker_2" "walker_3" "walker_4" ...
#>   .. ..$: chr [1:300] "generation_1" "generation_2" "generation_3" "generation_4" ... If the coda package is installed, you can then use the coda = TRUE argument to get objects of class mcmc.list. The coda package then allows you to call summary() and plot() to get informative and nicely formatted results and plots: ## use stretch move, return samples as 'coda' object res2 <- MCMCEnsemble(p.log, lower.inits=c(a=0, b=0), upper.inits=c(a=1, b=1), max.iter=3000, n.walkers=10, method="stretch", coda=TRUE) #> Using stretch move with 10 walkers. summary(res2$samples)
#>
#> Iterations = 1:300
#> Thinning interval = 1
#> Number of chains = 10
#> Sample size per chain = 300
#>
#> 1. Empirical mean and standard deviation for each variable,
#>    plus standard error of the mean:
#>
#>      Mean     SD Naive SE Time-series SE
#> a  2.5369 10.520  0.19206         1.2490
#> b -0.5984  4.575  0.08352         0.7117
#>
#> 2. Quantiles for each variable:
#>
#>     2.5%    25%   50%   75%  97.5%
#> a -17.10 -4.510 1.784 8.983 23.321
#> b -13.12 -2.091 1.080 2.633  4.066
plot(res2$samples) ## use different evolution move, return samples as 'coda' object res3 <- MCMCEnsemble(p.log, lower.inits=c(a=0, b=0), upper.inits=c(a=1, b=1), max.iter=3000, n.walkers=10, method="differential.evolution", coda=TRUE) #> Using differential.evolution move with 10 walkers. summary(res3$samples)
#>
#> Iterations = 1:300
#> Thinning interval = 1
#> Number of chains = 10
#> Sample size per chain = 300
#>
#> 1. Empirical mean and standard deviation for each variable,
#>    plus standard error of the mean:
#>
#>     Mean    SD Naive SE Time-series SE
#> a 0.8694 8.912  0.16270         0.8364
#> b 0.5526 3.038  0.05547         0.3271
#>
#> 2. Quantiles for each variable:
#>
#>      2.5%     25%    50%   75%  97.5%
#> a -16.052 -5.9044 0.4582 7.608 18.078
#> b  -8.126 -0.8741 1.4248 2.607  4.136
plot(res3\$samples)

## Parallel processing

This package is set up to allow transparent parallel processing when requested by the user thanks to the framework provided by the future package. To enable parallel processing, you must run:

future::plan("multiprocess")

at the start of your session.

## Similar projects

The methods used in this package also have (independent) implementations in other languages: