##### https://github.com/cran/bayestestR

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**d8462ad2168ad7ee61c0d7e679174e775f01a9be**authored by Dominique Makowski on**18 January 2020, 07:10 UTC**, committed by cran-robot on**18 January 2020, 07:10 UTC****1 parent**4936034

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**d8462ad2168ad7ee61c0d7e679174e775f01a9be**authored by**Dominique Makowski**on**18 January 2020, 07:10 UTC****version 0.5.0** Tip revision:

**d8462ad** distribution.Rd

```
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distribution.R
\name{distribution}
\alias{distribution}
\alias{distribution_normal}
\alias{distribution_binomial}
\alias{distribution_cauchy}
\alias{distribution_poisson}
\alias{distribution_student}
\alias{distribution_chisquared}
\alias{distribution_uniform}
\alias{distribution_beta}
\alias{distribution_tweedie}
\alias{distribution_gamma}
\alias{distribution_custom}
\alias{distribution_mixture_normal}
\alias{rnorm_perfect}
\title{Empirical Distributions}
\usage{
distribution(type = "normal", ...)
distribution_normal(n, mean = 0, sd = 1, random = FALSE, ...)
distribution_binomial(n, size = 1, prob = 0.5, random = FALSE, ...)
distribution_cauchy(n, location = 0, scale = 1, random = FALSE, ...)
distribution_poisson(n, lambda = 1, random = FALSE, ...)
distribution_student(n, df, ncp, random = FALSE, ...)
distribution_chisquared(n, df, ncp = 0, random = FALSE, ...)
distribution_uniform(n, min = 0, max = 1, random = FALSE, ...)
distribution_beta(n, shape1, shape2, ncp = 0, random = FALSE, ...)
distribution_tweedie(n, xi = NULL, mu, phi, power = NULL, random = FALSE, ...)
distribution_gamma(n, shape, scale = 1, random = FALSE, ...)
distribution_custom(n, type = "norm", ..., random = FALSE)
distribution_mixture_normal(n, mean = c(-3, 3), sd = 1, random = FALSE, ...)
rnorm_perfect(n, mean = 0, sd = 1)
}
\arguments{
\item{type}{Can be any of the names from base R's \link[stats]{Distributions}, like \code{"cauchy"}, \code{"pois"} or \code{"beta"}.}
\item{...}{Arguments passed to or from other methods.}
\item{n}{number of observations. If \code{length(n) > 1}, the length
is taken to be the number required.}
\item{mean}{vector of means.}
\item{sd}{vector of standard deviations.}
\item{random}{Generate near-perfect or random (simple wrappers for the base R \code{r*} functions) distributions.}
\item{size}{number of trials (zero or more).}
\item{prob}{probability of success on each trial.}
\item{location}{location and scale parameters.}
\item{scale}{location and scale parameters.}
\item{lambda}{vector of (non-negative) means.}
\item{df}{degrees of freedom (\eqn{> 0}, maybe non-integer). \code{df
= Inf} is allowed.}
\item{ncp}{non-centrality parameter \eqn{\delta}{delta};
currently except for \code{rt()}, only for \code{abs(ncp) <= 37.62}.
If omitted, use the central t distribution.}
\item{min}{lower and upper limits of the distribution. Must be finite.}
\item{max}{lower and upper limits of the distribution. Must be finite.}
\item{shape1}{non-negative parameters of the Beta distribution.}
\item{shape2}{non-negative parameters of the Beta distribution.}
\item{xi}{the value of \eqn{\xi}{xi} such that the variance is
\eqn{\mbox{var}[Y]=\phi\mu^{\xi}}{var(Y) = phi * mu^xi}}
\item{mu}{the mean}
\item{phi}{the dispersion}
\item{power}{a synonym for \eqn{\xi}{xi}}
\item{shape}{shape and scale parameters. Must be positive,
\code{scale} strictly.}
}
\description{
Generate a sequence of n-quantiles, i.e., a sample of size \code{n} with a near-perfect distribution.
}
\examples{
library(bayestestR)
x <- distribution(n = 10)
plot(density(x))
x <- distribution(type = "gamma", n = 100, shape = 2)
plot(density(x))
}
```

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