A package with general-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics
A package with general-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, particularly for process-based models.
The package contains 2 central functions,
createBayesianSetup, which creates a standardized Bayesian setup with likelihood and priors, and
runMCMC, which allows to run various MCMC and SMC samplers.
The package can of course also be used for general (non-Bayesian) target functions.
To use the package, a first step is to use
createBayesianSetup to create a BayesianSetup, which usually contains prior and likelihood densities, or in general a target function.
Those can be sampled with
runMCMC, which can call a number of general purpose Metropolis sampler, including the
Metropolis that allows to specify various popular Metropolis variants such as adaptive and/or delayed rejection Metropolis; two variants of differential evolution MCMC
DEzs, two variants of DREAM
Twalk MCMC, and a Sequential Monte Carlo sampler
The output of runMCMC is of class mcmcSampler / smcSampler if one run is performed, or mcmcSamplerList / smcSamplerList if several sampler are run. Various functions are available for plotting, model comparison (DIC, marginal likelihood), or to use the output as a new prior.
For details on how to use the packgage, run vignette("BayesianTools", package="BayesianTools").
To get the suggested citation, run citation("BayesianTools")
Acknowledgements: The creation and maintenance of this package profited from funding and collaboration through Cost Action FP 1304 PROFOUND, DFG DO 786/12-1 CONECT, EU FP7 ERA-NET Sumforest REFORCE and Bayklif Project BLIZ.
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