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.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://cran.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

1.70 score 459 downloads 9 exports 12 dependencies

Last updated from:ee94c085d9. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK163
linux-devel-x86_64OK157
source / vignettesOK198
linux-release-arm64OK158
linux-release-x86_64OK163
wasm-releaseOK117

Exports:drrglmini_parassimu_factor_model_datasimu_factor_model_parassimu_reg_coefssimu_reg_datasimu_zhouandli2014tune_drr_factor_modeltune_drrglm

Dependencies:codetoolsdata.tableforeachglmnetiteratorslatticeMatrixRcppRcppArmadilloRcppEigenshapesurvival