pcalg (2.4-5)

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.

Maintainer: Markus Kalisch
Author(s): Markus 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]

License: GPL (>= 2)

Uses: abind, bdsmatrix, clue, corpcor, fastICA, ggm, gmp, graph, igraph, RBGL, Rcpp, robustbase, sfsmisc, vcd, Matrix, mvtnorm, MASS
Reverse depends: qdg, qtlnet
Reverse suggests: backShift, CompareCausalNetworks, graphComp, iTOP, MXM, ParallelPC, qgraph, SCCI

Released almost 3 years ago.