Skip to content

EmmanuelCharpentier/LaplacesDemon

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

75 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

LaplacesDemon

A complete environment for Bayesian inference within R

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.

The main function in this package is the LaplacesDemon function, and the best place to start is probably with the LaplacesDemon Tutorial vignette.

Installation


Using the 'devtools' package:

install.packages("devtools")
library(devtools)
install_github("Statisticat/LaplacesDemon")

To install the latest official version, see Bayesian-Inference.com/SoftwareDownload.

About

A complete environment for Bayesian inference within R

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 98.0%
  • Perl 1.6%
  • Other 0.4%