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>.
