pcalg
type: Item
swMATH work ID: 6072
instance of: R package
software version identifier: 2.7-8
software version identifier: 0.1-3
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software version identifier: 2.7-11
CRAN project: pcalg
programmed in: R
described by source: Q5249586
described by source: Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
last update: 2024-02-12T00:00:00Z
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, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.
author: Alain Hauser
author: Martin Maechler
author: Markus Kalisch
copyright license: GNU General Public License, version 2.0
copyright license: GNU General Public License, version 3.0
imports: stats
imports: graphics
imports: utils
imports: methods
imports: abind
imports: graph
imports: RBGL
imports: igraph
imports: ggm
imports: corpcor
imports: robustbase
imports: vcd
imports: Rcpp
imports: bdsmatrix
imports: sfsmisc
imports: fastICA
imports: clue
MaRDI profile type: Software
depends on software: R

pcalg (source code repository)
source code repository: pcalg

pcalg (Software Heritage ID)
source code repository: pcalg
point in time: 2024-02-13T00:00:00Z

pcalg (implementation of)
implementation of: Peter-Clark algorithm

pcalg (implementation of)
implementation of: GES

pcalg (maintained by)
maintained by: Markus Kalisch