General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.


News

NOTE: for more news about the package, see https://github.com/florianhartig/BayesianTools/releases

BayesianTools 0.1.6

New features

Major changes

  • None

Minor changes

  • Help improvements and clarifications

Bugfixes

BayesianTools 0.1.5

Changes / improvements

  • marginalPlot changed / updated

Minor changes

Bugfixes:

BayesianTools 0.1.4

Compatibility

  • added NIMBLE support to getSample and plotting functions
  • added coda support to plotTimeSeriesResults and plotMarginals

Changes

  • IMPORTANT: added a warning when calling runMCMC with the "twalk" sampler. At the moment, using this sampler is discouraged
  • marginalPlot can now plot prior and/or posterior, either as histogram or as violin plots
  • added bridge sampling to marginalLikelihood
  • added shortcuts for AM, DR and DRAM samplers

Bugfixes:

  • when calling getSample on mcmcSamplerList, the chains are now merged instead of concatenated
  • formulas in the vignette are now rendered correctly
  • fixed a bug in VSEM help

Minor Changes:

  • changed the ordering of the summary metrics of summary.mcmcSampler, and summary.mcmcSamplerList
  • renamed marginalLikelihood output to "ln.ML"
  • added tests for plotTimeSeriesResults, marginalPlot and marginalLikelihood
  • added an example with restart to DEzs

BayesianTools 0.1.3

Compatibility

  • created compatibility to new Rcpp standard

Changes

  • getSample extended to coda objects, so that all plot functions can also be used on coda objects
  • plotTimeSeriesResults now includes the option to create residual plots from posterior predictive simulations, calling the DHARMa package
  • added new utitility functions sampleEquallySpaced and correctThin

Bugfixes

  • IMPORTANT: fixed a bug in createPriorDensity - results with createPriorDensity prior to this bugfix may be unrealiable
  • getSample now corrects wrong inputs for thin and numSamples instead of throwing an error
  • smaller bugfixes in plot functions

BayesianTools 0.1.2

Bugfixes

  • various smaller improvement / bugfixes in getSample, plot functions / Help (0.1.2)
  • warnings / bugfixes associated to burnin arguments (0.1.1.2)
  • bugfix in marginal plots (0.1.1.1)

BayesianTools 0.1.1

Mostly a bugfix release

Minor changes

  • implemented method for estimating posterior volume (0.1.0.1)
  • implemented GOF (0.1.0.2)

Bugfixes

  • removed erroneously created help files (0.1.0.1)
  • dynload change to conform to R 3.4.x requirements (0.1.1)
  • smaller bugxfixes and help updates (0.1.1)

BayesianTools 0.1.0

  • initial release

Reference manual

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install.packages("BayesianTools")

0.1.6 by Florian Hartig, 3 months ago


https://github.com/florianhartig/BayesianTools


Report a bug at https://github.com/florianhartig/BayesianTools/issues


Browse source code at https://github.com/cran/BayesianTools


Authors: Florian Hartig [aut, cre] , Francesco Minunno [aut] , Stefan Paul [aut] , David Cameron [ctb] , Tankred Ott [ctb] , Maximilian Pichler [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports coda, emulator, mvtnorm, tmvtnorm, IDPmisc, Rcpp, ellipse, numDeriv, msm, MASS, Matrix, stats, utils, graphics, DHARMa, gap, bridgesampling

Suggests DEoptim, lhs, sensitivity, knitr, rmarkdown, roxygen2, testthat

Linking to Rcpp


See at CRAN