The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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) <doi:10.48550/arXiv.2202.01697>.
Version: | 0.4.1 |
Depends: | R (≥ 2.10) |
Imports: | BBmisc, EnvStats, Formula, gamlss.dist, GeneralizedHyperbolic, methods, nleqslv, stats, VGAM, zipfR |
Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0) |
Published: | 2023-02-16 |
DOI: | 10.32614/CRAN.package.PLreg |
Author: | Felipe Queiroz [aut, cre], Silvia Ferrari [aut] |
Maintainer: | Felipe Queiroz <ffelipeq at outlook.com> |
License: | GPL (≥ 3) |
URL: | https://github.com/ffqueiroz/PLreg |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | PLreg results |
Reference manual: | PLreg.pdf |
Package source: | PLreg_0.4.1.tar.gz |
Windows binaries: | r-devel: PLreg_0.4.1.zip, r-release: PLreg_0.4.1.zip, r-oldrel: PLreg_0.4.1.zip |
macOS binaries: | r-release (arm64): PLreg_0.4.1.tgz, r-oldrel (arm64): PLreg_0.4.1.tgz, r-release (x86_64): PLreg_0.4.1.tgz, r-oldrel (x86_64): PLreg_0.4.1.tgz |
Old sources: | PLreg archive |
Please use the canonical form https://CRAN.R-project.org/package=PLreg to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.