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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 |
| 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 |
| 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 |
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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.