Title:	Ensemble Sampler for Affine-Invariant MCMC
Version:	3.2.0
Description:	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) <doi:10.1007/s11222-008-9104-9> and the 'stretch' sampler from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.
License:	GPL-2
URL:	https://hugogruson.fr/mcmcensemble/, https://github.com/Bisaloo/mcmcensemble
BugReports:	https://github.com/Bisaloo/mcmcensemble/issues
Depends:	R (≥ 3.5)
Imports:	future.apply, progressr
Suggests:	bayesplot, coda, mockery, testthat (≥ 3.0.0), knitr, rmarkdown, mvtnorm
Encoding:	UTF-8
RoxygenNote:	7.3.2
Config/testthat/edition:	3
Config/Needs/website:	spacefillr
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-24 08:02:11 UTC; hugo
Author:	Hugo Gruson [cre, aut, cph], Sanda Dejanic [aut, cph], Andreas Scheidegger [aut, cph]
Maintainer:	Hugo Gruson <hugo.gruson+R@normalesup.org>
Repository:	CRAN
Date/Publication:	2025-07-24 08:40:17 UTC

MCMCEnsembleSampler

Description

This package implements a particle Monte Carlo Markov Chain sampler with two different ways of creating proposals.

Author(s)

Maintainer: Hugo Gruson hugo.gruson+R@normalesup.org (ORCID) [copyright holder]

Authors:

• Sanda Dejanic [copyright holder]

• Andreas Scheidegger (ORCID) [copyright holder]

See Also

Useful links:

• https://hugogruson.fr/mcmcensemble/

• https://github.com/Bisaloo/mcmcensemble

• Report bugs at https://github.com/Bisaloo/mcmcensemble/issues

MCMC ensemble sampler

Description

Ensemble Markov Chain Monte Carlo sampler with different strategies to generate proposals. Either the stretch move as proposed by Goodman and Weare (2010), or a differential evolution jump move similar to Braak and Vrugt (2008).

Usage

MCMCEnsemble(
  f,
  inits,
  max.iter,
  n.walkers = 10 * ncol(inits),
  method = c("stretch", "differential.evolution"),
  coda = FALSE,
  ...
)

Arguments

Value

• if coda = FALSE a list with:

  • samples: A three dimensional array of samples with dimensions walker x generation x parameter

  • log.p: A matrix with the log density evaluate for each walker at each generation.

• if coda = TRUE a list with:

  • samples: A object of class coda::mcmc.list containing all samples.

  • log.p: A matrix with the log density evaluate for each walker at each generation.

In both cases, there is an additional attribute (accessible via attr(res, "ensemble.sampler")) recording which ensemble sampling algorithm was used.

References

• ter Braak, C. J. F. and Vrugt, J. A. (2008) Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435–446, doi:10.1007/s11222-008-9104-9

• Goodman, J. and Weare, J. (2010) Ensemble samplers with affine invariance. Communications in Applied Mathematics and Computational Science, 5(1), 65–80, doi:10.2140/camcos.2010.5.65

Examples

## 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
}

## set options and starting point
n_walkers <- 10
unif_inits <- data.frame(
  "a" = runif(n_walkers, 0, 1),
  "b" = runif(n_walkers, 0, 1)
)


## use stretch move
res1 <- MCMCEnsemble(p.log, inits = unif_inits,
                     max.iter = 150, n.walkers = n_walkers,
                     method = "stretch")

attr(res1, "ensemble.sampler")

str(res1)


## use stretch move, return samples as 'coda' object
res2 <- MCMCEnsemble(p.log, inits = unif_inits,
                     max.iter = 150, n.walkers = n_walkers,
                     method = "stretch", coda = TRUE)

attr(res2, "ensemble.sampler")

summary(res2$samples)
plot(res2$samples)


## use different evolution move, return samples as 'coda' object
res3 <- MCMCEnsemble(p.log, inits = unif_inits,
                     max.iter = 150, n.walkers = n_walkers,
                     method = "differential.evolution", coda = TRUE)

attr(res3, "ensemble.sampler")

summary(res3$samples)
plot(res3$samples)