nonlinearICP: Invariant Causal Prediction for Nonlinear Models

Performs 'nonlinear Invariant Causal Prediction' to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending 'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', <arXiv:1706.08576>.

Version: 0.1.2.1
Depends: R (≥ 3.1.0)
Imports: methods, CondIndTests, data.tree, caTools, randomForest
Suggests: testthat
Published: 2017-07-31
Author: Christina Heinze-Deml, Jonas Peters
Maintainer: Christina Heinze-Deml <heinzedeml at stat.math.ethz.ch>
BugReports: https://github.com/christinaheinze/nonlinearICP-and-CondIndTests/issues
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://github.com/christinaheinze/nonlinearICP-and-CondIndTests
NeedsCompilation: no
Citation: nonlinearICP citation info
CRAN checks: nonlinearICP results

Downloads:

Reference manual: nonlinearICP.pdf
Package source: nonlinearICP_0.1.2.1.tar.gz
Windows binaries: r-devel: nonlinearICP_0.1.2.1.zip, r-devel-gcc8: nonlinearICP_0.1.2.1.zip, r-release: nonlinearICP_0.1.2.1.zip, r-oldrel: nonlinearICP_0.1.2.1.zip
OS X binaries: r-release: nonlinearICP_0.1.2.1.tgz, r-oldrel: nonlinearICP_0.1.2.1.tgz
Old sources: nonlinearICP archive

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