Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

The DESCRIPTION file:
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The goal of LaplacesDemon, often referred to as LD, is to provide a complete and self-contained Bayesian environment within R. For example, this package includes dozens of MCMC algorithms, Laplace Approximation, iterative quadrature, variational Bayes, parallelization, big data, PMC, over 100 examples in the “Examples” vignette, dozens of additional probability distributions, numerous MCMC diagnostics, Bayes factors, posterior predictive checks, a variety of plots, elicitation, parameter and variable importance, Bayesian forms of test statistics (such as Durbin-Watson, Jarque-Bera, etc.), validation, and numerous additional utility functions, such as functions for multimodality, matrices, or timing your model specification. Other vignettes include an introduction to Bayesian inference, as well as a tutorial.

No further development of this package is currently being done as the original maintainer has stopped working on the package. Contributions to this package are welcome at https://github.com/LaplacesDemonR/LaplacesDemon.

The main function in this package is the `LaplacesDemon`

function, and the best place to start is probably with the
LaplacesDemon Tutorial vignette.

Byron Hall [aut], Martina Hall [aut], Statisticat, LLC [aut], Eric Brown [ctb], Richard Hermanson [ctb], Emmanuel Charpentier [ctb], Daniel Heck [ctb], Stephane Laurent [ctb], Quentin F. Gronau [ctb], Henrik Singmann [cre]

Maintainer: Henrik Singmann <singmann+LaplacesDemon@gmail.com>

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