nonlinearicp-and-condindtests

Code for a variety of nonlinear conditional independence tests and '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) to nonlinear settings.

https://github.com/christinaheinze/nonlinearicp-and-condindtests

Science Score: 10.0%

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    Links to: arxiv.org
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    Low similarity (5.6%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Code for a variety of nonlinear conditional independence tests and '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) to nonlinear settings.

Basic Info
Statistics
  • Stars: 17
  • Watchers: 2
  • Forks: 4
  • Open Issues: 3
  • Releases: 0
Created about 9 years ago · Last pushed over 6 years ago
Metadata Files
Readme

README.md

R packages 'nonlinearICP' and 'CondIndTests'

R Code for 'nonlinearICP' and 'CondIndTests'.

CondIndTests

Code for a variety of nonlinear conditional independence tests:

  • Kernel conditional independence test (Zhang et al., UAI 2011),
  • Residual Prediction test (based on Shah and Buehlmann, arXiv 2015),
  • Invariant environment prediction,
  • Invariant target prediction,
  • Invariant residual distribution test,
  • Invariant conditional quantile prediction (all from Heinze-Deml et al., arXiv:1706.08576).

Installation

From CRAN

r install.packages("CondIndTests")

From Github with devtools

r devtools::install_github("christinaheinze/nonlinearICP-and-CondIndTests/CondIndTests")

nonlinearICP

Code for '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) to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', arXiv:1706.08576.

Installation

From CRAN

r install.packages("nonlinearICP")

From Github with devtools

r devtools::install_github("christinaheinze/nonlinearICP-and-CondIndTests/nonlinearICP")

References

If you are using these packages, please cite C. Heinze-Deml, J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', arXiv:1706.08576.

Owner

  • Name: Christina Heinze-Deml
  • Login: christinaheinze
  • Kind: user

I am a Research Scientist at Apple Health AI. My research interests include causality, distributional robustness and deep representation learning.

GitHub Events

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Last Year

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 46
  • Total Committers: 2
  • Avg Commits per committer: 23.0
  • Development Distribution Score (DDS): 0.109
Top Committers
Name Email Commits
Christina Heinze-Deml t****e@g****m 41
Jonas Peters j****s@m****u 5
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 12 months ago

All Time
  • Total issues: 2
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 0.5
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • kingfish777 (1)
  • mvergara89 (1)
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Packages

  • Total packages: 2
  • Total downloads:
    • cran 520 last-month
  • Total docker downloads: 43,226
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 6
  • Total maintainers: 1
cran.r-project.org: CondIndTests

Nonlinear Conditional Independence Tests

  • Versions: 4
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 275 Last month
  • Docker Downloads: 21,613
Rankings
Forks count: 12.5%
Stargazers count: 14.0%
Dependent packages count: 18.0%
Dependent repos count: 24.7%
Average: 24.8%
Downloads: 54.9%
Last synced: 12 months ago
cran.r-project.org: nonlinearICP

Invariant Causal Prediction for Nonlinear Models

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 245 Last month
  • Docker Downloads: 21,613
Rankings
Forks count: 12.8%
Stargazers count: 14.2%
Dependent packages count: 29.8%
Average: 32.1%
Dependent repos count: 35.5%
Downloads: 68.0%
Last synced: 12 months ago

Dependencies

CondIndTests/DESCRIPTION cran
  • R >= 3.1.0 depends
  • MASS * imports
  • RPtests * imports
  • caTools * imports
  • kernlab * imports
  • lawstat * imports
  • methods * imports
  • mgcv * imports
  • mize * imports
  • pracma * imports
  • quantregForest * imports
  • randomForest * imports
  • testthat * suggests
nonlinearICP/DESCRIPTION cran
  • R >= 3.1.0 depends
  • CondIndTests * imports
  • caTools * imports
  • data.tree * imports
  • methods * imports
  • randomForest * imports
  • testthat * suggests