We are pleased to release Version 1.3-7 of the MCMCpack package. This is a major release of MCMCpack that contains a dynamic item response theory model as well, some new models for Bayesian time series analysis, a Bayesian quantile regression function, and heteroscedastic item response theory model. All models are now coded using Scythe 1.0.2, which would make estimation substantially faster. It also contains our first Bayesian time series model coded in C++. We are happy to add Jong Hee Park as a full collaborator on the MCMCpack project. This release (mostly) conforms to the developer environment described in the specification and uses the newest version of the Scythe Statistical Library. It also supports parallel computing using suitable random number generators, which are now licensed under the GNU GPL. This site contains both the source code for Linux, Unix, and MacOS X installations. A binary file for Windows will become available on Comprehensive R Archive Network. This version of MCMCpack depends on version 0.11-3 or greater of the coda package, which is available from the Comprehensive R Archive Network, R version 2.10.0 or greater, and gcc 4.0 or greater.
Installing MCMCpack from source on a Linux, Unix, or MacOS X workstation is easy. First, download the package source MCMCpack_1.3-7.tar.gz. Then, login as superuser and type:
R CMD INSTALL MCMCpack_1.3-7.tar.gz
To install MCMCpack on Windows, we recommend downloading the binary file from CRAN.
One can also very easily install the latest version of MCMCpack available at CRAN on any machine (regardless of platform) with a live internet connection. To do this, open R, and then at the R command prompt issue the following command: