Title Methods for Graphical Models and Causal Inference

March 19, 2015 Version 2.0-10 Date 2015-03-18 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch Title Methods for Graphical Models and Causal Inference Description 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. Depends R (>= 3.0.2) LinkingTo Rcpp (>= 0.11.0), RcppArmadillo, BH Imports graphics, utils, methods, abind, graph, RBGL, igraph, ggm, corpcor, robustbase, vcd, Rcpp, bdsmatrix, sfsmisc Suggests MASS, Matrix, Rgraphviz, mvtnorm ByteCompile yes NeedsCompilation yes Encoding UTF-8 License GPL (>= 2)