R Development Page

Contributed R Packages

Below is a list of all packages provided by project pcalg - Interventioneffects using graphs.

Important note for package binaries: R-Forge provides these binaries only for the most recent version of R, but not for older versions. In order to successfully install the packages provided on R-Forge, you have to switch to the most recent version of R or, alternatively, install from the package sources (.tar.gz).



Methods for Graphical Models and Causal Inference

  Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.
  Version: 2.4-6 | Last change: 2017-04-26 14:23:58+02 | Rev.: 421
  Download: linux(.tar.gz) | windows(.zip) | Build status: Current | Stable Release: Get pcalg 2.4-5 from CRAN
  R install command: install.packages("pcalg", repos="")
Package build: Source package (Linux x86_64) Windows binary (x86_64/i386)
Package check: Linux x86_64 (patched) | Linux x86_64 (devel) Windows (patched) | Windows (devel)


Build status codes

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