pcalg: Methods for Graphical Models and Causal Inference
Version 2.4-5

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.

Package details

AuthorMarkus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb]
Date of publication2017-02-22 09:57:28
MaintainerMarkus Kalisch <kalisch@stat.math.ethz.ch>
LicenseGPL (>= 2)
Version2.4-5
URL http://pcalg.r-forge.r-project.org/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("pcalg")

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pcalg documentation built on May 29, 2017, 4:05 p.m.