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Welcome to dcr - Data Cloning in R project!

Data cloning (DC) uses Bayesian MCMC to make maximum likelihood inference of complex hierarchical models. The bundle includes basic infrastructure for DC with parallel computing support, and more specialized packages for ecology.

The project summary page you can find here.

The stable dclone release you can find at CRAN.

Documentation

Infrastructure for dclone

Because dclone is an R package, installing R is absolutely necessary. The choice of the preferred BUGS program is up to the user.

The dclone package itself depends on the coda (>= 0.13), R2WinBUGS, and parallel which are all available from CRAN. The dclone package also suggests to have the rjags (>= 3-2), snow, rlecuyer, rsprng, BRugs R packages (the latter two might not be available for all platforms).

The rjags package is suggested for using JAGS. The rjags dependency of dclone was removed so that other functionality of the package can be used without JAGS being installed (rjags can't load without JAGS).

The easiest way to install the stable release and all required packages at once is to type this after opening R:

install.packages(c("dclone", "rjags"))

The development version can be installed as:

install.packages("dclone", repos = "http://r-forge.r-project.org")

Parallel computing

Parallel computing is supported via clusters or multiple cores. Clusters defined by the snow or parallel packages can be used to run multiple parallel MCMC chains by JAGS. Forking type parallelism via the parallel package is also supported.

Parallel computing for data cloning is provided either via parallel chains, `size balancing' (see this tutorial for a general overview), or a combination of the the two. Size balancing is available for WinBUGS/OpenBUGS as well.

Specialized packages in this repository