R package for performing Bayesian inference, including various MCMC and SMC sampling algorithms!
BayesianTools is on CRAN (see here). To install the latest CRAN release, type
install.packages("BayesianTools")
To get an overview about its functionality once the package is installed, run
library(BayesianTools)
?BayesianTools
vignette("BayesianTools", package="BayesianTools")
As for every R package, you can get the suggested citation via
citation("BayesianTools")
If you want to test / install a newer package version from GitHub, note that there are two versions you may consider
The master branch contains the latest, stable version of the package. It will typically be very close to the CRAN version, and it should be save to install it. It is possible that the main branch contains subreleases that are not pushed to CRAN (see https://github.com/florianhartig/BayesianTools/releases)
Status master
Install via
devtools::install_github(repo = "florianhartig/BayesianTools", subdir = "BayesianTools", dependencies = T, build_vignettes = T)
The development branch contains new features and improvements, but may not be fully tested.
Status development
If you want to install the current (development) version from this repository, run
devtools::install_github(repo = "florianhartig/BayesianTools", subdir = "BayesianTools", dependencies = T, build_vignettes = T, ref="development")
Windows users: the package contains c++ code, so if you compile yourself, you need RTools installed.
To install a specific (older) release, decide for the version number that you want to install in https://github.com/florianhartig/BayesianTools/releases (version numbering corresponds to CRAN, but there may be smaller releases that were not pushed to CRAN) and run, e.g.
devtools::install_github(repo = "florianhartig/BayesianTools", subdir = "BayesianTools", ref = "v0.0.10", dependencies = T, build_vignettes = T)
with v0.0.10 replaced by the appropriate version number.
We highly welcome questions by users, so don't be shy - any questions, even if it feels "stupid", helps us to understand how we can improve the interface, documentation, or code of the package.
If you want to ask a question or report a bug, the most convenient way for us would be to provide a reproducible example via the GitHub issues
Work on this package was facilicated through meetings of Cost Action FP 1304 Profound.