PLreg: Power Logit Regression for Modeling Bounded Data
Fitting 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) <arXiv:2202.01697>.
| Version: |
0.2.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
BBmisc, EnvStats, Formula, gamlss.dist, GeneralizedHyperbolic, methods, nleqslv, stats, VGAM, zipfR |
| Suggests: |
rmarkdown, knitr, testthat (≥ 3.0.0) |
| Published: |
2022-03-30 |
| 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 |
| CRAN checks: |
PLreg results |
Documentation:
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