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**.

- 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).

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 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.

**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]