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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
DOI: 10.32614/CRAN.package.kpcalg
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:

Please use the canonical form https://CRAN.R-project.org/package=kpcalg to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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