Package: dfr 0.1.2
dfr: Dual Feature Reduction for SGL
Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.
Authors:
dfr_0.1.2.tar.gz
dfr_0.1.2.tar.gz(r-4.5-noble)dfr_0.1.2.tar.gz(r-4.4-noble)
dfr_0.1.2.tgz(r-4.4-emscripten)dfr_0.1.2.tgz(r-4.3-emscripten)
dfr.pdf |dfr.html✨
dfr/json (API)
# Install 'dfr' in R: |
install.packages('dfr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ff1201/dfr/issues
Last updated 28 days agofrom:0567bb5677. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 28 2024 |
R-4.5-linux | OK | Nov 28 2024 |
Exports:dfr_adap_sgldfr_adap_sgl.cvdfr_sgldfr_sgl.cv
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadillorecipesreshape2RlabrlangrpartscalessgsshapeSLOPESQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr