Package: drrglm 0.3.2

Zengchao Xu

drrglm: Doubly Regularized Matrix-Variate Regression

The doubly regularized matrix-variate regression solves a low-rank-plus-sparse structure for matrix-variate generalized linear models through a weighted combination of nuclear-norm and L1-norm. The methodology implemented by this package is described in the paper "Doubly Regularized Matrix-Variate Regression", which has been tentatively accepted for publication but does not yet have a DOI or URL. A formal citation will be added in a future update once the final publication details are available.

Authors:Zengchao Xu [aut, cre, cph], Shan Luo [aut], Binyan Jiang [aut]

drrglm_0.3.2.tar.gz
drrglm_0.3.2.zip(r-4.7)drrglm_0.3.2.zip(r-4.6)drrglm_0.3.2.zip(r-4.5)
drrglm_0.3.2.tgz(r-4.6-x86_64)drrglm_0.3.2.tgz(r-4.6-arm64)drrglm_0.3.2.tgz(r-4.5-x86_64)drrglm_0.3.2.tgz(r-4.5-arm64)
drrglm_0.3.2.tar.gz(r-4.7-arm64)drrglm_0.3.2.tar.gz(r-4.7-x86_64)drrglm_0.3.2.tar.gz(r-4.6-arm64)drrglm_0.3.2.tar.gz(r-4.6-x86_64)
drrglm_0.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
drrglm/json (API)
NEWS

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

Bug tracker:https://github.com/paradoxical-rhapsody/drrglm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • EEG - ElectroEncephaloGraphy Data

On CRAN:

Conda:

openblascpp

3.54 score 569 downloads 9 exports 12 dependencies

Last updated from:43013eb2a9. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK144
linux-devel-x86_64OK144
source / vignettesOK178
linux-release-arm64OK131
linux-release-x86_64OK118
macos-release-arm64OK124
macos-release-x86_64OK199
macos-oldrel-arm64OK178
macos-oldrel-x86_64OK246
windows-develOK146
windows-releaseOK125
windows-oldrelOK160
wasm-releaseOK107

Exports:drrglmini_parassimu_factor_model_datasimu_factor_model_parassimu_reg_coefssimu_reg_datasimu_zhouandli2014tune_drr_factor_modeltune_drrglm

Dependencies:codetoolsdata.tableforeachglmnetiteratorslatticeMatrixRcppRcppArmadilloRcppEigenshapesurvival