LaplacesDemon
The DESCRIPTION file: LaplacesDemon LaplacesDemon
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.