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>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README
CRAN checks: PLreg results


Reference manual: PLreg.pdf


Package source: PLreg_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PLreg_0.2.0.tgz, r-oldrel (arm64): PLreg_0.2.0.tgz, r-release (x86_64): PLreg_0.2.0.tgz, r-oldrel (x86_64): PLreg_0.2.0.tgz


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