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
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
- Host: GitHub
- Owner: christinaheinze
- Language: R
- Default Branch: master
- Homepage: https://arxiv.org/abs/1706.08576
- Size: 178 KB
Statistics
- Stars: 17
- Watchers: 2
- Forks: 4
- Open Issues: 3
- Releases: 0
Metadata Files
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
- Website: https://christinaheinze.github.io/
- Repositories: 5
- Profile: https://github.com/christinaheinze
I am a Research Scientist at Apple Health AI. My research interests include causality, distributional robustness and deep representation learning.
GitHub Events
Total
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 | 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)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- cran 520 last-month
- Total docker downloads: 43,226
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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
- Homepage: https://github.com/christinaheinze/nonlinearICP-and-CondIndTests
- Documentation: http://cran.r-project.org/web/packages/CondIndTests/CondIndTests.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL]
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Latest release: 0.1.5
published over 6 years ago
Rankings
Maintainers (1)
cran.r-project.org: nonlinearICP
Invariant Causal Prediction for Nonlinear Models
- Homepage: https://github.com/christinaheinze/nonlinearICP-and-CondIndTests
- Documentation: http://cran.r-project.org/web/packages/nonlinearICP/nonlinearICP.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL]
-
Latest release: 0.1.2
published about 9 years ago
Rankings
Maintainers (1)
Dependencies
- 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
- R >= 3.1.0 depends
- CondIndTests * imports
- caTools * imports
- data.tree * imports
- methods * imports
- randomForest * imports
- testthat * suggests