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>.

Getting started

Package details

AuthorFelipe Queiroz [aut, cre], Silvia Ferrari [aut]
MaintainerFelipe Queiroz <>
LicenseGPL (>= 3)
Package repositoryView on CRAN
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PLreg documentation built on Feb. 16, 2023, 7:26 p.m.