Package: PrivateLR 1.2-22
PrivateLR: Differentially Private Regularized Logistic Regression
Implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006 <doi:10.1007/11681878_14>), if |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon for any pair D, D' of datasets that differ in exactly one record, any measurable set S, and the randomness is taken over the choices F makes.
Authors:
PrivateLR_1.2-22.tar.gz
PrivateLR_1.2-22.zip(r-4.5)PrivateLR_1.2-22.zip(r-4.4)PrivateLR_1.2-22.zip(r-4.3)
PrivateLR_1.2-22.tgz(r-4.4-any)PrivateLR_1.2-22.tgz(r-4.3-any)
PrivateLR_1.2-22.tar.gz(r-4.5-noble)PrivateLR_1.2-22.tar.gz(r-4.4-noble)
PrivateLR_1.2-22.tgz(r-4.4-emscripten)PrivateLR_1.2-22.tgz(r-4.3-emscripten)
PrivateLR.pdf |PrivateLR.html✨
PrivateLR/json (API)
| # Install 'PrivateLR' in R: |
| install.packages('PrivateLR', repos = c('https://laats.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:186f6945da. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 15 2024 |
| R-4.5-win | OK | Nov 15 2024 |
| R-4.5-linux | OK | Nov 15 2024 |
| R-4.4-win | OK | Nov 15 2024 |
| R-4.4-mac | OK | Nov 15 2024 |
| R-4.3-win | OK | Nov 15 2024 |
| R-4.3-mac | OK | Nov 15 2024 |
Exports:dplrdplr.data.framedplr.factordplr.formuladplr.logicaldplr.matrixdplr.numericpredict.dplrprint.dplrprint.summary.dplrscaledsummary.dplr
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Differentially Private Logistic Regression | dplr dplr.data.frame dplr.factor dplr.formula dplr.logical dplr.matrix dplr.numeric predict.dplr print.dplr print.summary.dplr PrivateLR scaled summary.dplr |
