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

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**23ea3229abe72a5f23dcf3a4cfcd3478d744b536**authored by Dominique Makowski on**20 June 2019, 11:50 UTC**, committed by cran-robot on**20 June 2019, 11:50 UTC****1 parent**9985109

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**23ea3229abe72a5f23dcf3a4cfcd3478d744b536**authored by**Dominique Makowski**on**20 June 2019, 11:50 UTC****version 0.2.2** Tip revision:

**23ea322** 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_cauchy}
\alias{distribution_poisson}
\alias{distribution_student}
\alias{distribution_chisquared}
\alias{distribution_uniform}
\alias{distribution_beta}
\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_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_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 \code{"normal"} (default), \code{"cauchy"}, \code{"poisson"}, \code{"chisquared"}, \code{"uniform"}, \code{"student"} 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{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.}
}
\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))
}
```

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