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

Downloads:

Reference manual: kpcalg.pdf
Vignettes: kpcalg tutorial
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
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: kpcalg_1.0.1.tgz

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