Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.
| Version: | 2.1.6 |
| Depends: | Matrix, R (≥ 3.2) |
| Imports: | methods |
| Published: | 2020-02-26 |
| Author: | Luigi Augugliaro [aut, cre], Angelo Mineo [aut], Ernst Wit [aut], Hassan Pazira [aut], Michael Wichura [ctb, cph], John Burkardt [ctb, cph] |
| Maintainer: | Luigi Augugliaro <luigi.augugliaro at unipa.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.jstatsoft.org/v59/i08/. |
| NeedsCompilation: | yes |
| Citation: | dglars citation info |
| Materials: | ChangeLog |
| CRAN checks: | dglars results |
| Reference manual: | dglars.pdf |
| Package source: | dglars_2.1.6.tar.gz |
| Windows binaries: | r-devel: dglars_2.1.6.zip, r-release: dglars_2.1.6.zip, r-oldrel: dglars_2.1.6.zip |
| macOS binaries: | r-release: dglars_2.1.6.tgz, r-oldrel: dglars_2.1.6.tgz |
| Old sources: | dglars archive |
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