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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>.

Versions across snapshots

VersionRepositoryFileSize
0.4.1 rolling linux/jammy R-4.5 PLreg_0.4.1.tar.gz 186.3 KiB
0.4.1 rolling linux/noble R-4.5 PLreg_0.4.1.tar.gz 186.2 KiB
0.4.1 rolling source/ R- PLreg_0.4.1.tar.gz 46.4 KiB
0.4.1 latest linux/jammy R-4.5 PLreg_0.4.1.tar.gz 186.3 KiB
0.4.1 latest linux/noble R-4.5 PLreg_0.4.1.tar.gz 186.2 KiB
0.4.1 latest source/ R- PLreg_0.4.1.tar.gz 46.4 KiB
0.4.1 2026-04-26 source/ R- PLreg_0.4.1.tar.gz 46.4 KiB
0.4.1 2026-04-23 source/ R- PLreg_0.4.1.tar.gz 46.4 KiB
0.4.1 2026-04-09 windows/windows R-4.5 PLreg_0.4.1.zip 189.3 KiB
0.4.1 2025-04-20 source/ R- PLreg_0.4.1.tar.gz 46.4 KiB

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