Package: gofar 0.1

Aditya Mishra

gofar: Generalized Co-Sparse Factor Regression

Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.

Authors:Aditya Mishra [aut, cre], Kun Chen [aut]

gofar_0.1.tar.gz
gofar_0.1.tar.gz(r-4.7-arm64)gofar_0.1.tar.gz(r-4.7-x86_64)gofar_0.1.tar.gz(r-4.6-arm64)gofar_0.1.tar.gz(r-4.6-x86_64)
gofar_0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
gofar/json (API)

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

Bug tracker:https://github.com/amishra-stats/gofar/issues

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

On CRAN:

Conda:

openblascpp

1.00 score 5 scripts 213 downloads 4 exports 31 dependencies

Last updated from:bf1aa33c37. Checks:4 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE148
linux-devel-x86_64NOTE145
source / vignettesOK199
linux-release-arm64NOTE150
linux-release-x86_64NOTE154
wasm-releaseOK121

Exports:gofar_controlgofar_pgofar_sgofar_sim

Dependencies:clicodetoolscpp11farverforeachggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrrpackS7scalesshapesurvivalvctrsviridisLitewithr