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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 |
| 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 |
| Reference manual: | gofar.pdf |
| 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 |
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