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mcmcensemble 3.2.0

Bug fixes

• a bug resulting in weak correlation across walkers has been resolved (#9, @scheidan). Runs from this version will therefore produce results different than with previous versions, even for a given seed.

Minor improvements

Two changes ensure chains don’t get stuck in a loop:

• an early parameter check ensures that sufficient walkers are provided (via the n.walkers argument) to ensure ergodicity (#11, @scheidan)
• noise has been added to each step of the differential evolution algorithm to ensure we don’t end up walking on a grid (#12, @scheidan)

mcmcensemble 3.1.0

Minor improvements

• there is now a clearer error message when trying to use a single walker since ensemble sampling is designed to work with multiple walkers (#6 by @Bisaloo, based on a report from @adamkucharski).

mcmcensemble 3.0.0

Major changes

• the arguments lower.inits and upper.inits are deprecated in favour of inits which leave more flexibility to the user. Please read the detailed blog post for more background about this change and how to migrate.
• inits can now be a data.frame or a matrix
• d.e.mcmc() and s.m.mcmc() are not exported any more. Please use the wrapper MCMCEnsemble() instead.
• there is a new vignette (vignette("diagnostic-pkgs", package = "mcmcensemble")) presenting two different options (coda and bayesplot) to plot and evaluate the MCMC chains produced by mcmcensemble.

Bug fixes

• The chains now run fine even in the case where there is only one iteration (i.e., max.iter %/% n.walkers == 1)
• The error message when the coda package is absent and coda = TRUE now correctly prompt the user to use coda = FALSE if they do not wish to install coda.

mcmcensemble 2.2.0

Major changes

• it is now possible to use a named vector as first argument of the function passed in f. This is useful if you do something like:

p.log.named <- function(x) {
  B <- 0.03
  return(-x["a"]^2/200 - 1/2*(x["b"]+B*x["a"]^2-100*B)^2)
}

• mcmcensemble now explicitly depends on R >= 3.5.0. This was already implicitly the case since 2.1 because of the dependency on the progressr package.
• the ensemble sampling algorithm used by MCMCEnsemble() is now recorded in an additional attribute (accessible via attr(res, "ensemble.sampler")).

Other user-facing changes

• there is now an additional argument check ensuring that lower.inits and upper.inits have the same names

mcmcensemble 2.1

Major changes

• the ensemble sampling can now be parallelised with the future framework. Check the README for more information

Other user-facing changes

• very large log.p differences between chains do not cause them to be stuck any more
• addition of a new vignette listing frequently asked questions (with their answer)

Dev changes

• new test to make sure the chains converge as expected
• performance improvements

mcmcensemble 2.0

Breaking changes

• The argument names and order in d.e.mcmc() and s.m.mcmc() now match those of MCMCEnsemble()

Other user-facing changes

• coda package is now only in Suggests, instead of being a hard dependency

Dev changes

• this package is now named mcmcensemble
• roxygen2 documentation now uses markdown syntax
• this package now has unit and regression tests
• various parts of the code have been optimized for speed

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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