Package: nonlinearICP 0.1.2.1

Christina Heinze-Deml

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>.

Authors:Christina Heinze-Deml <[email protected]>, Jonas Peters <[email protected]>

nonlinearICP_0.1.2.1.tar.gz
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nonlinearICP.pdf |nonlinearICP.html✨
nonlinearICP/json (API)

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

Peer review:

Bug tracker:https://github.com/christinaheinze/nonlinearicp-and-condindtests/issues

Datasets:
  • simData - Example dataset for tests

On CRAN:

3.93 score 17 stars 10 scripts 134 downloads 2 exports 32 dependencies

Last updated 5 years agofrom:e6808046f2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-winOKNov 04 2024
R-4.5-linuxOKNov 04 2024
R-4.4-winOKNov 04 2024
R-4.4-macOKNov 04 2024
R-4.3-winOKNov 04 2024
R-4.3-macOKNov 04 2024

Exports:nonlinearICPvarSelectionRF

Dependencies:bitopsbootcaToolscodetoolsCondIndTestsdata.treeforeachglmnetiteratorsKendallkernlablatticelawstatMASSMatrixmgcvmizemvtnormnlmepracmaquantregForestR6randomForestrbibutilsRColorBrewerRcppRcppEigenRdpackRPtestsshapestringisurvival