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:Staal A. Vinterbo <[email protected]>

PrivateLR_1.2-22.tar.gz
PrivateLR_1.2-22.zip(r-4.7)PrivateLR_1.2-22.zip(r-4.6)PrivateLR_1.2-22.zip(r-4.5)
PrivateLR_1.2-22.tgz(r-4.6-any)PrivateLR_1.2-22.tgz(r-4.5-any)
PrivateLR_1.2-22.tar.gz(r-4.7-any)PrivateLR_1.2-22.tar.gz(r-4.6-any)
PrivateLR_1.2-22.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
PrivateLR/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 scripts 150 downloads 12 exports 0 dependencies

Last updated from:186f6945da. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK95
source / vignettesOK132
linux-release-x86_64OK93
macos-release-arm64OK158
macos-oldrel-arm64OK153
windows-develOK61
windows-releaseOK60
windows-oldrelOK55
wasm-releaseOK90

Exports:dplrdplr.data.framedplr.factordplr.formuladplr.logicaldplr.matrixdplr.numericpredict.dplrprint.dplrprint.summary.dplrscaledsummary.dplr

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Differentially Private Logistic Regressiondplr dplr.data.frame dplr.factor dplr.formula dplr.logical dplr.matrix dplr.numeric predict.dplr print.dplr print.summary.dplr PrivateLR scaled summary.dplr