gamlss.dist: Distributions for Generalized Additive Models for Location Scale and Shape

A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a "log" or a "logit" transformation respectively.

Version: 5.0-4
Depends: R (≥ 2.15.0), MASS, graphics, stats, methods
Published: 2017-12-11
Author: c(person("Mikis", "Stasinopoulos", role = c("aut", "cre", "cph"), email = ""), person("Bob", "Rigby", role = c("aut")), person("Calliope", "Akantziliotou", role = "ctb"), person("Vlasios", "Voudouris", role = "ctb", email= ""), person("Gillian", "Heller", role = "ctb"), person("Raydonal", "Ospina", role = "ctb", email= ""), person("Nicoletta", "Motpan", role = "ctb"), person("Fiona", "McElduff", role = "ctb"), person("Majid", "Djennad", role = "ctb"), person("Marco", "Enea", role = "ctb"), person("Alexios", "Ghalanos", role = "ctb"), person("Christos", "Argyropoulos", role = "ctb") )
Maintainer: Mikis Stasinopoulos <mikis.stasinopoulos at>
License: GPL-2 | GPL-3
NeedsCompilation: yes
In views: Distributions
CRAN checks: gamlss.dist results


Reference manual: gamlss.dist.pdf
Package source: gamlss.dist_5.0-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: gamlss.dist_5.0-4.tgz
OS X Mavericks binaries: r-oldrel: gamlss.dist_5.0-4.tgz
Old sources: gamlss.dist archive

Reverse dependencies:

Reverse depends: acid, binequality, gamlss, gamlss.add, gamlss.cens, gamlss.demo,, gamlss.spatial,, gamlss.util, gamlssbssn, SemiParSampleSel
Reverse imports: AGD, childsds, GJRM, ImputeRobust, list, powdist, PReMiuM, rpql, Wrapped, ZIBseq
Reverse suggests: depmixS4, fitdistrplus, gamboostLSS, hnp, mazeinda, PerformanceAnalytics


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