Package: sgs 0.2.0

Fabio Feser

sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general adaptive three operator splitting (ATOS) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) models are also implemented. Both gSLOPE and SGS are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Authors:Fabio Feser [aut, cre], Marina Evangelou [aut]

sgs_0.2.0.tar.gz
sgs_0.2.0.tar.gz(r-4.5-noble)sgs_0.2.0.tar.gz(r-4.4-noble)
sgs_0.2.0.tgz(r-4.4-emscripten)sgs_0.2.0.tgz(r-4.3-emscripten)
sgs.pdf |sgs.html✨
sgs/json (API)

Peer review:

Bug tracker:https://github.com/ff1201/sgs/issues

11 exports 1 stars 0.23 score 86 dependencies 10 scripts 210 downloads

Last updated 2 months agofrom:3945a16b11. Checks:OK: 2. Indexed: no.

Exports:arma_mvarma_sparseas_sgsatosfit_gslopefit_gslope_cvfit_sgsfit_sgs_cvgen_pensgen_toy_datascaled_sgs

Dependencies:bootcaretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverfauxforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatrixmgcvminqaModelMetricsmunsellnlmenloptrnnetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrecipesreshape2RlabrlangrpartscalesshapeSLOPESQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetruncnormtzdbutf8vctrsviridisLitewithr