recipe r-pcalg

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), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.



GPL2 / GPL-2



package r-pcalg

(downloads) docker_r-pcalg



depends bioconductor-graph:

depends bioconductor-rbgl:

depends libgcc-ng:


depends libstdcxx-ng:


depends r-abind:

depends r-base:


depends r-bdsmatrix:

depends r-bh:

depends r-clue:

depends r-corpcor:

depends r-dagitty:

depends r-fastica:

depends r-ggm:

depends r-igraph:

depends r-rcpp:

depends r-rcpparmadillo:

depends r-robustbase:

depends r-sfsmisc:


depends r-vcd:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install r-pcalg

and update with::

   mamba update r-pcalg

To create a new environment, run:

mamba create --name myenvname r-pcalg

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `r-pcalg/tags`_ for valid values for ``<tag>``)

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