PLreg: Power Logit Regression for Modeling Bounded Data
Power logit regression models for bounded continuous data, in which the density generator may be normal, Student-t, power exponential, slash, hyperbolic, sinh-normal, or type II logistic. Diagnostic tools associated with the fitted model, such as the residuals, local influence measures, leverage measures, and goodness-of-fit statistics, are implemented. The estimation process follows the maximum likelihood approach and, currently, the package supports two types of estimators: the usual maximum likelihood estimator and the penalized maximum likelihood estimator. More details about power logit regression models are described in Queiroz and Ferrari (2022) <doi:10.48550/arXiv.2202.01697>.
| Version: | 0.4.1 |
| Depends: | R (≥ 2.10) |
| Imports: | BBmisc,EnvStats,Formula,gamlss.dist,GeneralizedHyperbolic, methods,nleqslv, stats,VGAM,zipfR |
| Suggests: | rmarkdown,knitr,testthat (≥ 3.0.0) |
| Published: | 2023-02-16 |
| DOI: | 10.32614/CRAN.package.PLreg |
| Author: | Felipe Queiroz [aut, cre], Silvia Ferrari [aut] |
| Maintainer: | Felipe Queiroz <ffelipeq at outlook.com> |
| License: | GPL (≥ 3) |
| URL: | https://github.com/ffqueiroz/PLreg |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | PLreg results |
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