Package: kpcalg 1.0.1

Petras Verbyla

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.

Authors:Petras Verbyla, Nina Ines Bertille Desgranges, Lorenz Wernisch

kpcalg_1.0.1.tar.gz
kpcalg_1.0.1.tar.gz(r-4.5-noble)kpcalg_1.0.1.tar.gz(r-4.4-noble)
kpcalg_1.0.1.tgz(r-4.4-emscripten)kpcalg_1.0.1.tgz(r-4.3-emscripten)
kpcalg.pdf |kpcalg.html✨
kpcalg/json (API)

# Install 'kpcalg' in R:
install.packages('kpcalg', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.79 score 2 stars 31 scripts 208 downloads 12 exports 42 dependencies

Last updated 8 years agofrom:4dc04cc6d7. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 16 2024
R-4.5-linuxNOTEOct 16 2024

Exports:dcov.gammafrml.additive.smoothfrml.full.smoothhsic.clusthsic.gammahsic.permhsic.testkernelCItestkpcregrVonPSregrXonSudag2wanpdag

Dependencies:abindbdsmatrixBHBiocGenericsBiocManagerbootcliclueclustercolorspacecorpcorcpp11DEoptimRenergyfastICAggmgluegraphgsligraphkernlablatticelifecyclelmtestmagrittrMASSMatrixmgcvnlmepcalgpkgconfigRBGLRcppRcppArmadilloRcppEigenrlangrobustbaseRSpectrasfsmiscvcdvctrszoo

kpcalg tutorial

Rendered fromkpcalg_tutorial.Rnwusingknitr::knitron Oct 16 2024.

Last update: 2017-01-22
Started: 2017-01-22