PLreg
0.4.1Power Logit Regression for Modeling Bounded Data
Overview
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.
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Package metadata
- First published
- 2022-03-30
- Total releases
- 5 / 4 yrs
- License
- GPL (>= 3) OSI
- Download size
- 46 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet