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) <arXiv:2202.01697>.

Version: 0.3.1
Depends: R (≥ 2.10)
Imports: BBmisc, EnvStats, Formula, gamlss.dist, GeneralizedHyperbolic, methods, nleqslv, stats, VGAM, zipfR
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0)
Published: 2023-01-23
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

Documentation:

Reference manual: PLreg.pdf

Downloads:

Package source: PLreg_0.3.1.tar.gz
Windows binaries: r-devel: PLreg_0.3.0.zip, r-release: PLreg_0.3.1.zip, r-oldrel: PLreg_0.3.1.zip
macOS binaries: r-release (arm64): PLreg_0.3.1.tgz, r-oldrel (arm64): PLreg_0.3.1.tgz, r-release (x86_64): PLreg_0.3.1.tgz, r-oldrel (x86_64): PLreg_0.3.1.tgz
Old sources: PLreg archive

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