Package: PLreg 0.4.0

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>.

Authors:Felipe Queiroz [aut, cre], Silvia Ferrari [aut]

PLreg_0.4.0.tar.gz
PLreg_0.4.0.zip(r-4.7-any)PLreg_0.4.0.zip(r-4.6-any)PLreg_0.4.0.zip(r-4.5-any)
PLreg_0.4.0.tgz(r-4.6-any)PLreg_0.4.0.tgz(r-4.5-any)
PLreg_0.4.0.tar.gz(r-4.7-any)PLreg_0.4.0.tar.gz(r-4.6-any)
PLreg_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
PLreg/json (API)

# Install 'PLreg' in R:
install.packages('PLreg', repos = c('https://ffqueiroz.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ffqueiroz/plreg/issues

Datasets:

On CRAN:

Conda:

2.70 score 2 scripts 285 downloads 11 exports 31 dependencies

Last updated from:78cb146d92. Checks:2 NOTE, 7 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE162
source / vignettesOK169
linux-releaseOK160
macos-releaseOK188
macos-oldrelOK165
windows-develNOTE133
windows-releaseOK150
windows-oldrelOK137
wasm-releaseOK115

Exports:CI.lambdadPLenvelopeextra.parameterinfluencePLregPLreg.controlpPLqPLrPLsandwich

Dependencies:backportsBBmisccheckmateclicpp11data.tableDistributionUtilsEnvStatsfarverFormulagamlss.distGeneralizedHyperbolicggplot2gluegtableisobandlabelinglifecycleMASSnleqslvnortestR6RColorBrewerrlangS7scalesvctrsVGAMviridisLitewithrzipfR