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-4.5-noble)gofar_0.1.tar.gz(r-4.4-noble)
gofar_0.1.tgz(r-4.4-emscripten)gofar_0.1.tgz(r-4.3-emscripten)
gofar.pdf |gofar.html✨
gofar/json (API)

# Install 'gofar' in R:
install.packages('gofar', repos = '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-Forge:

openblascpp

1.00 score 1 scripts 188 downloads 4 exports 38 dependencies

Last updated 3 years agofrom:bf1aa33c37. Checks:1 OK, 1 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 19 2025
R-4.5-linux-x86_64NOTEFeb 19 2025

Exports:gofar_controlgofar_pgofar_sgofar_sim

Dependencies:clicodetoolscolorspacefansifarverforeachggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrrpackscalesshapesurvivaltibbleutf8vctrsviridisLitewithr