pcalg (2.0-10)

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 and the generalized backdoor criterion is implemented.

Maintainer: Markus Kalisch
Author(s): Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler

License: GPL (>= 2)

Uses: Does not use any package
Reverse depends: qdg, qtlnet
Reverse suggests: backShift, CompareCausalNetworks, graphComp, iTOP, MXM, ParallelPC, qgraph, SCCI

Released over 4 years ago.