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 360 downloads 4 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK131
linux-devel-x86_64OK172
source / vignettesOK214
linux-release-arm64OK130
linux-release-x86_64OK161
macos-release-arm64OK117
macos-release-x86_64OK445
macos-oldrel-arm64OK191
macos-oldrel-x86_64OK281
windows-devel-arm64NOTE281
windows-devel-x86_64NOTE209
windows-release-arm64NOTE241
windows-release-x86_64NOTE179
windows-oldrel-x86_64FAIL83
wasm-releaseOK120

Exports:cpgcv.cpgcv.ProxGradProxGrad

Dependencies:RcppRcppArmadillo