Welcome to LaplacesDemon, a complete environment for Bayesian inference within R.
| Package: | LaplacesDemon |
| Type: | Package |
| Version: | 16.00 |
| Date: | 2016-07-16 |
| License: | MIT |
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.
There are many plans for the growth of this package, and many are long-term plans such as to cotinuously stockpile distributions, examples, samplers, and optimization algorithms. 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.