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PLreg
View on CRAN: Click
here
Download and install PLreg package within the R console
Install from CRAN:
install.packages("PLreg")
Install from Github:
library("remotes")
install_github("cran/PLreg") Install by package version:
library("remotes")
install_version("PLreg", "0.4.1") Attach the package and use:
library("PLreg")
Maintained by
Felipe Queiroz
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-03-30
Latest Update: 2023-02-16
Description:
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) .
How to cite:
Felipe Queiroz (2022). PLreg: Power Logit Regression for Modeling Bounded Data. R package version 0.4.1, https://cran.r-project.org/web/packages/PLreg. Accessed 28 Oct. 2026.
Previous versions and publish date:
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Other R packages that PLreg depends,
imports, suggests or enhances
Complete documentation for PLreg
Functions, R codes and Examples using
the PLreg R package
Some associated functions: Firm . PL . PLreg . PLregcontrol . PeruVotes . bodyfat_Aeolus . envelope . extra.parameter . influence . methodsPLreg . plot.PLreg . residuals.PLreg . sandwich .
Some associated R codes: Firm.R . PLdist.R . PLreg.R . PeruVotes.R . bodyfat_Aeolus.R . Full PLreg package functions and examples
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