Title: The Pareto, Piecewise Pareto and Generalized Pareto Distribution
Description: Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution

Suggests: testthat, knitr, rmarkdown, lpSolve

Title: Computing, fitting and validating the Pareto Positive Stable
Authors@R: c(person("Antonio Jose", "Saez-Castillo", role = c("aut", "cre"),
person("Faustino", "Prieto", role = "aut",
person("Jose Maria", "Sarabia", role = "aut",
Description: Statistical functions to describe a Pareto Positive Stable distribution and fit it to real data. Graphical and statistical tools to validate the fits are included.

Authors@R: c(person("Antonio Jose", "Saez-Castillo", role = c("aut", "cre"), email = "ajsaez@ujaen.es"),
person("Faustino", "Prieto", role = "aut", email = "faustino.prieto@unican.es"),
person("Jose Maria", "Sarabia", role = "aut", email = "jose.sarabia@unican.es"))

Title: Computing, fitting and validating the PPS distribution
Description: Statistical functions to describe a Pareto Positive Stable (PPS)
distribution and fit it to real data. Graphical and statistical tools to

Title: Computing, Fitting and Validating the PPS Distribution
Imports: graphics, grDevices, stats, ADGofTest, lmom, foreach,

Title: Partially Censored Regression Models Based on Heavy-Tailed
Author: Marcela Nunez Lemus, Christian E. Galarza, Larissa Avila Matos, Victor H Lachos
Maintainer: Marcela Nunez Lemus <ra162510@ime.unicamp.br>
Description: It estimates the parameters of a partially censored regression model via maximum penalized likelihood through a iterative EM-type algorithm. The model must belong to the semi-parametric family, including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the student's-t distribution among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.