kpcalg: Kernel PC Algorithm for Causal Structure Detection

Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise.

Version:	1.0.1
Depends:	R (≥ 3.0.2)
Imports:	pcalg, energy, kernlab, parallel, mgcv, RSpectra, methods, graph, stats, utils
Suggests:	Rgraphviz, knitr
Published:	2017-01-22
Author:	Petras Verbyla, Nina Ines Bertille Desgranges, Lorenz Wernisch
Maintainer:	Petras Verbyla <petras.verbyla at mrc-bsu.cam.ac.uk>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	kpcalg results

Documentation:

Reference manual:	kpcalg.pdf
Vignettes:	kpcalg tutorial

Downloads:

Package source:	kpcalg_1.0.1.tar.gz
Windows binaries:	r-devel: kpcalg_1.0.1.zip, r-release: kpcalg_1.0.1.zip, r-oldrel: kpcalg_1.0.1.zip
macOS binaries:	r-release (arm64): kpcalg_1.0.1.tgz, r-oldrel (arm64): kpcalg_1.0.1.tgz, r-release (x86_64): kpcalg_1.0.1.tgz, r-oldrel (x86_64): kpcalg_1.0.1.tgz

Linking:

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