distrEx-package {distrEx} | R Documentation |
distrEx provides some extensions of package distr:
expectations in the form
E(X)
for the expectation of a
distribution object X
E(X,f)
for the expectation of f(X)
where X
is some distribution object and
f
some function in X
further functionals: var, sd, IQR, mad, median, skewness, kurtosis
truncated moments,
distances between distributions (Hellinger, Cramer von Mises, Kolmogorov, total variation, "convex contamination")
lists of distributions,
conditional distributions in factorized form
conditional expectations in factorized form
Support for extreme value distributions has moved to package RobExtremes
Package: | distrEx |
Version: | 2.9.2 |
Date: | 2024-01-29 |
Depends: | R(>= 3.4), methods, distr(>= 2.8.0) |
Imports: | startupmsg, utils, stats |
Suggests: | tcltk |
LazyLoad: | yes |
License: | LGPL-3 |
URL: | https://distr.r-forge.r-project.org/ |
VCS/SVNRevision: | 1426 |
Distribution Classes "Distribution" (from distr) |>"UnivariateDistribution" (from distr) |>|>"AbscontDistribution" (from distr) |>|>|>"Gumbel" (moved to package 'RobExtremes') |>|>|>"Pareto" (moved to package 'RobExtremes') |>|>|>"GPareto" (moved to package 'RobExtremes') |>"MultivariateDistribution" |>|>"DiscreteMVDistribution-class" |>"UnivariateCondDistribution" |>|>"AbscontCondDistribution" |>|>|>"PrognCondDistribution" |>|>"DiscreteCondDistribution" Condition Classes "Condition" |>"EuclCondition" |>"PrognCondition" Parameter Classes "OptionalParameter" (from distr) |>"Parameter" (from distr) |>|>"LMParameter" |>|>"GumbelParameter" |>|>"ParetoParameter"
Integration: GLIntegrate Gauss-Legendre quadrature distrExIntegrate Integration of one-dimensional functions Options: distrExOptions Function to change the global variables of the package 'distrEx' Standardization: make01 Centering and standardization of univariate distributions
Distribution Classes ConvexContamination Generic function for generating convex contaminations DiscreteMVDistribution Generating function for DiscreteMVDistribution-class Gumbel Generating function for Gumbel-class LMCondDistribution Generating function for the conditional distribution of a linear regression model. Condition Classes EuclCondition Generating function for EuclCondition-class Parameter Classes LMParameter Generating function for LMParameter-class
Distances: ContaminationSize Generic function for the computation of the convex contamination (Pseudo-)distance of two distributions HellingerDist Generic function for the computation of the Hellinger distance of two distributions KolmogorovDist Generic function for the computation of the Kolmogorov distance of two distributions TotalVarDist Generic function for the computation of the total variation distance of two distributions AsymTotalVarDist Generic function for the computation of the asymmetric total variation distance of two distributions (for given ratio rho of negative to positive part of deviation) OAsymTotalVarDist Generic function for the computation of the minimal (in rho) asymmetric total variation distance of two distributions vonMisesDist Generic function for the computation of the von Mises distance of two distributions liesInSupport Generic function for testing the support of a distribution Functionals: E Generic function for the computation of (conditional) expectations var Generic functions for the computation of functionals IQR Generic functions for the computation of functionals sd Generic functions for the computation of functionals mad Generic functions for the computation of functionals median Generic functions for the computation of functionals skewness Generic functions for the computation of functionals kurtosis Generic functions for the computation of Functionals truncated Moments: m1df Generic function for the computation of clipped first moments m2df Generic function for the computation of clipped second moments
Demos are available — see demo(package="distrEx")
.
G. Jay Kerns, gkerns@ysu.edu, has provided a major contribution,
in particular the functionals skewness
and kurtosis
are due to him.
You may suppress the start-up banner/message completely by setting
options("StartupBanner"="off")
somewhere before loading this package by
library
or require
in your R-code / R-session.
If option "StartupBanner"
is not defined (default) or setting
options("StartupBanner"=NULL)
or
options("StartupBanner"="complete")
the complete start-up banner is
displayed.
For any other value of option "StartupBanner"
(i.e., not in
c(NULL,"off","complete")
) only the version information is displayed.
The same can be achieved by wrapping the library
or require
call
into either suppressStartupMessages()
or
onlytypeStartupMessages(.,atypes="version")
.
As for general packageStartupMessage
's, you may also suppress all
the start-up banner by wrapping the library
or require
call into suppressPackageStartupMessages()
from
startupmsg-version 0.5 on.
Note: The first two numbers of package versions do not necessarily reflect package-individual development, but rather are chosen for the distrXXX family as a whole in order to ease updating "depends" information.
Some functions of package stats have intentionally been masked, but
completely retain their functionality — see distrExMASK()
.
If any of the packages e1071, moments, fBasics is to be used
together with distrEx the latter must be attached after any of the
first mentioned. Otherwise kurtosis()
and skewness()
defined as methods in distrEx may get masked.
To re-mask, you
may use kurtosis <- distrEx::kurtosis; skewness <- distrEx::skewness
.
See also distrExMASK()
Matthias Kohl Matthias.Kohl@stamats.de and
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de,
Maintainer: Matthias Kohl Matthias.Kohl@stamats.de
P. Ruckdeschel, M. Kohl, T. Stabla, F. Camphausen (2006): S4 Classes for Distributions, R News, 6(2), 2-6. https://CRAN.R-project.org/doc/Rnews/Rnews_2006-2.pdf a vignette for packages distr, distrSim, distrTEst,
and distrEx is included into the mere documentation package distrDoc
and may be called by require("distrDoc");vignette("distr")
a homepage to this package is available under
https://distr.r-forge.r-project.org/
M. Kohl (2005): Numerical Contributions to the Asymptotic
Theory of Robustness. PhD Thesis. Bayreuth. Available as
https://www.stamats.de/wp-content/uploads/2018/04/ThesisMKohl.pdf