Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso. Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS. <http://proceedings.mlr.press/v84/takada18a/takada18a.pdf>.
| Version: | 0.0.2 |
| Imports: | Rcpp, Matrix |
| LinkingTo: | Rcpp, BH |
| Suggests: | testthat, knitr, rmarkdown, MASS, parallel |
| Published: | 2018-06-21 |
| Author: | Masaaki Takada |
| Maintainer: | Masaaki Takada <tkdmah at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | http://proceedings.mlr.press/v84/takada18a/takada18a.pdf |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | iilasso results |
| Reference manual: | iilasso.pdf |
| Vignettes: |
Introduction to iilasso package |
| Package source: | iilasso_0.0.2.tar.gz |
| Windows binaries: | r-devel: iilasso_0.0.2.zip, r-release: iilasso_0.0.2.zip, r-oldrel: iilasso_0.0.2.zip |
| macOS binaries: | r-release (arm64): iilasso_0.0.2.tgz, r-oldrel (arm64): iilasso_0.0.2.tgz, r-release (x86_64): iilasso_0.0.2.tgz, r-oldrel (x86_64): iilasso_0.0.2.tgz |
| Old sources: | iilasso archive |
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