Efficient algorithms for fitting weighted least squares regression with \eqn{L_{1}}{L1} regularization on both the coefficients and weight vectors, which is able to perform simultaneous variable selection and outliers detection efficiently.
| Version: | 1.0.0 |
| Suggests: | mvtnorm |
| Published: | 2017-05-11 |
| Author: | Bin Luo, Xiaoli Gao |
| Maintainer: | Bin Luo <b_luo at uncg.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | pawls results |
| Reference manual: | pawls.pdf |
| Package source: | pawls_1.0.0.tar.gz |
| Windows binaries: | r-devel: pawls_1.0.0.zip, r-release: pawls_1.0.0.zip, r-oldrel: pawls_1.0.0.zip |
| OS X binaries: | r-release: pawls_1.0.0.tgz, r-oldrel: pawls_1.0.0.tgz |
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