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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

• Short introduction: Péter Sólymos, 2010. dclone: Data Cloning in R. The R Journal, 2(2):29-37, December 2010. URL http://journal.R-project.org/ [bib] [pdf] [local copy] [R code] (Official citation)
• Reference manual: [pdf] (Detailed technical documentation of the package from CRAN)
• Additional resources: tutorials (under development), worked examples (also available via the dcmle package), extras (not fully supported and experimental features).

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.

• R (>= 2.14.0): download from CRAN.
• WinBUGS (>= 1.4): download from here, don't forget the patch and the immortality key, the R2WinBUGS and coda R packages are required for using WinBUGS from within R.
• OpenBUGS: download from here, it requires the BRugs (>= 0.3-2) (or the R2OpenBUGS) R package .
• JAGS (>= 3.0.0): download from here, the rjags and coda R packages are required for using JAGS from within R.

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

• dcmle: Hierarchical Models Made Easy with Data Cloning. S4 classes around infrastructure provided by the dclone package to make package development with data cloning for hierarchical models easy as a breeze. [get it from CRAN]
• pbapply: a lightweight package that adds progress bar to vectorized R functions ('*apply'). The implenentation can easily be added to functions, where showing the progress is useful for the user (e.g. bootstrap). [get it from CRAN]
• sharx: data sets and SAR, SARX, HSAR and HSARX models as described in Solymos and Lele (in press). [get it from CRAN]
• ResourceSelection: Resource Selection (Probability) Functions for use-availability wildlife data as described in Lele and Keim (2006, Ecology 87, 3021–3028), and Lele (2009, J. Wildlife Management 73, 122–127). [get it from CRAN]
• detect: Analyzing wildlife data with detection error. The package implements models to analyze site occupancy and count data models with detection error. [get it from CRAN]
• PVAClone: Likelihood based population viability analysis in the presence of observation error and missing data. The package can be used to fit, compare, predict, and forecast various growth model types using data cloning. [get it from CRAN]