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kpcalg  

Kernel PC Algorithm for Causal Structure Detection
View on CRAN: Click here


Download and install kpcalg package within the R console
Install from CRAN:
install.packages("kpcalg")

Install from Github:
library("remotes")
install_github("cran/kpcalg")

Install by package version:
library("remotes")
install_version("kpcalg", "1.0.1")



Attach the package and use:
library("kpcalg")
Maintained by
Petras Verbyla
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-01-22
Latest Update: 2017-01-22
Description:
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.
How to cite:
Petras Verbyla (2017). kpcalg: Kernel PC Algorithm for Causal Structure Detection. R package version 1.0.1, https://cran.r-project.org/web/packages/kpcalg. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for kpcalg
Functions, R codes and Examples using the kpcalg R package
Some associated functions: dcov.gamma . frml.additive.smooth . frml.full.smooth . hsic.clust . hsic.gamma . hsic.perm . hsic.test . kernelCItest . kpc . regrVonPS . regrXonS . udag2wanpdag . 
Some associated R codes: dcovgamma.R . frmladditivesmooth.R . frmlfullsmooth.R . hsicclust.R . hsicgamma.R . hsicperm.R . hsictest.R . kernelCItest.R . kpc.R . regrXonS.R . regrvonps.R . udag2wanpdag.R .  Full kpcalg package functions and examples
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