Packaged: Wed Feb 25 08:59:30 2009; hornik

Title: Probability Distributions as S3 Objects
Description: Tools to create and manipulate probability
distributions using S3.  Generics random(), pdf(), cdf() and
quantile() provide replacements for base R's r/d/p/q style functions.
Functions and arguments have been named carefully to minimize
confusion for students in intro stats courses. The documentation for
each distribution contains detailed mathematical notes.
Suggests: covr, cowplot, ggplot2, knitr, rmarkdown, testthat (>= 2.1.0)
Author: Alex Hayes [aut, cre] (<https://orcid.org/0000-0002-4985-5160>),

Suggests: covr, cowplot, knitr, revdbayes (>= 1.3.5), rmarkdown,

person("Alex", "Hayes", , "alexpghayes@gmail.com", role = c("aut", "cre"),
person("Ralph", "Moller-Trane", role = "aut"),
person("Emil", "Hvitfeldt", , "emilhhvitfeldt@gmail.com", role = "ctb",
person("Daniel", "Jordan", , "dandermotj@gmail.com", role = "aut"),
person("Paul", "Northrop", , "p.northrop@ucl.ac.uk", role = "aut"),
person("Moritz N.", "Lang", , "moritz.n.lang@gmail.com", role = "aut",
person("Achim", "Zeileis", , "Achim.Zeileis@R-project.org", role = "aut",
person("Bruna", "Wundervald", , "brunadaviesw@gmail.com", role = "ctb"),
person("Alessandro", "Gasparini", , "alessandro.gasparini@ki.se", role = "ctb")
Description: Tools to create and manipulate probability distributions
using S3.  Generics random(), pdf(), cdf() and quantile() provide
replacements for base R's r/d/p/q style functions.  Functions and
arguments have been named carefully to minimize confusion for students
in intro stats courses. The documentation for each distribution

using S3.  Generics pdf(), cdf(), quantile(), and random() provide
replacements for base R's d/p/q/r style functions.  Functions and

Title: Distribution Fitting and Evaluation
Authors@R: person("Ruben", "Dewitte", email = "ruben0dewitte@gmail.com", role = c("aut", "cre"))
Description: A library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.
Imports: Rdpack, stats, flexmix, modeltools, methods
Suggests: knitr, rmarkdown, tidyverse, testthat