privateEC-package: Welcome to privateEC
In insilico/privateEC: Privacy Preserving Evaporative Cooling Feature Selection and Classification with Relief-F and Random Forests
Description
Package Algorithms
- Simulation
simulate data sets: mainEffect/interactionErdos/interactionScalefree
- Classification
privateEC and comparison algorithms
- nestedCV
nested cross validation methods
- Workflow
functions for reproducing paper analysis pipelines
References
Trang Le, W. K. Simmons, M. Misaki, B.C. White, J. Savitz, J. Bodurka, and
B. A. McKinney. “Differential privacy-based Evaporative Cooling feature selection
and classification with Relief-F and Random Forests,” Bioinformatics.
Accepted. https://doi.org/10.1093/bioinformatics/btx298. 2017.
This work has been supported in part by NIMH/NIH grants (R01 MH098099 to JB,
and K01 MH096175 to WKS).
insilico/privateEC documentation built on May 22, 2020, 5:12 p.m.
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Description
Package Algorithms
- Simulation
simulate data sets: mainEffect/interactionErdos/interactionScalefree
- Classification
privateEC and comparison algorithms
- nestedCV
nested cross validation methods
- Workflow
functions for reproducing paper analysis pipelines
References
Trang Le, W. K. Simmons, M. Misaki, B.C. White, J. Savitz, J. Bodurka, and B. A. McKinney. “Differential privacy-based Evaporative Cooling feature selection and classification with Relief-F and Random Forests,” Bioinformatics. Accepted. https://doi.org/10.1093/bioinformatics/btx298. 2017.
This work has been supported in part by NIMH/NIH grants (R01 MH098099 to JB, and K01 MH096175 to WKS).
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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