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"))

Computing, fitting and validating the PPS distribution
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"))
Statistical functions to describe a Pareto Positive Stable (PPS)
distribution and fit it to real data. Graphical and statistical tools to
validate the fits are included.

Computing, Fitting and Validating the PPS Distribution
graphics, grDevices, stats, ADGofTest, lmom, foreach,
doParallel, parallel

Partially Censored Regression Models Based on Heavy-Tailed
Distributions
Marcela Nunez Lemus, Christian E. Galarza, Larissa Avila Matos, Victor H Lachos
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.

Estimation and Diagnostics for Partially Linear Censored
Regression Models Based on Heavy-Tailed Distributions
It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that 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-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.