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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.

Version: 0.1
Depends: R (≥ 3.5), stats, utils
Imports: Rcpp (≥ 0.12.9), MASS, magrittr, rrpack, glmnet
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-03-02
DOI: 10.32614/CRAN.package.gofar
Author: Aditya Mishra [aut, cre], Kun Chen [aut]
Maintainer: Aditya Mishra <amishra at flatironinstitute.org>
License: GPL (≥ 3.0)
URL: https://github.com/amishra-stats/gofar, https://www.sciencedirect.com/science/article/pii/S0167947320302188
NeedsCompilation: yes
Language: en-US
CRAN checks: gofar results

Documentation:

Reference manual: gofar.pdf

Downloads:

Package source: gofar_0.1.tar.gz
Windows binaries: r-devel: gofar_0.1.zip, r-release: gofar_0.1.zip, r-oldrel: gofar_0.1.zip
macOS binaries: r-release (arm64): gofar_0.1.tgz, r-oldrel (arm64): gofar_0.1.tgz, r-release (x86_64): gofar_0.1.tgz, r-oldrel (x86_64): gofar_0.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=gofar to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.