Package: CPGLIB 1.1.1

CPGLIB: Competing Proximal Gradients Library

Functions to generate ensembles of generalized linear models using competing proximal gradients. The optimal sparsity and diversity tuning parameters are selected via an alternating grid search.

Authors:Anthony Christidis [aut, cre], Stefan Van Aelst [aut], Ruben Zamar [aut]

CPGLIB_1.1.1.tar.gz
CPGLIB_1.1.1.zip(r-4.7-x86_64)CPGLIB_1.1.1.zip(r-4.7-arm64)CPGLIB_1.1.1.zip(r-4.6-x86_64)CPGLIB_1.1.1.zip(r-4.6-arm64)
CPGLIB_1.1.1.tgz(r-4.6-x86_64)CPGLIB_1.1.1.tgz(r-4.6-arm64)CPGLIB_1.1.1.tgz(r-4.5-x86_64)CPGLIB_1.1.1.tgz(r-4.5-arm64)
CPGLIB_1.1.1.tar.gz(r-4.7-arm64)CPGLIB_1.1.1.tar.gz(r-4.7-x86_64)CPGLIB_1.1.1.tar.gz(r-4.6-arm64)CPGLIB_1.1.1.tar.gz(r-4.6-x86_64)
CPGLIB_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CPGLIB/json (API)

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

Bug tracker:https://github.com/anthonychristidis/cpglib/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

2.70 score 2 scripts 359 downloads 4 exports 2 dependencies

Last updated from:67b7206cc3. Checks:10 OK, 4 NOTE, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK153
linux-devel-x86_64OK172
source / vignettesOK209
linux-release-arm64OK158
linux-release-x86_64OK169
macos-release-arm64OK118
macos-release-x86_64OK263
macos-oldrel-arm64OK160
macos-oldrel-x86_64OK319
windows-devel-arm64NOTE342
windows-devel-x86_64NOTE219
windows-release-arm64NOTE237
windows-release-x86_64NOTE245
windows-oldrel-x86_64FAIL75
wasm-releaseOK139

Exports:cpgcv.cpgcv.ProxGradProxGrad

Dependencies:RcppRcppArmadillo