Generate a sequence of n-quantiles, i.e., a sample of size `n`

with a
near-perfect distribution.

## Usage

```
distribution(type = "normal", ...)
distribution_custom(n, type = "norm", ..., random = FALSE)
distribution_beta(n, shape1, shape2, ncp = 0, random = FALSE, ...)
distribution_binomial(n, size = 1, prob = 0.5, random = FALSE, ...)
distribution_binom(n, size = 1, prob = 0.5, random = FALSE, ...)
distribution_cauchy(n, location = 0, scale = 1, random = FALSE, ...)
distribution_chisquared(n, df, ncp = 0, random = FALSE, ...)
distribution_chisq(n, df, ncp = 0, random = FALSE, ...)
distribution_gamma(n, shape, scale = 1, random = FALSE, ...)
distribution_mixture_normal(n, mean = c(-3, 3), sd = 1, random = FALSE, ...)
distribution_normal(n, mean = 0, sd = 1, random = FALSE, ...)
distribution_gaussian(n, mean = 0, sd = 1, random = FALSE, ...)
distribution_nbinom(n, size, prob, mu, phi, random = FALSE, ...)
distribution_poisson(n, lambda = 1, random = FALSE, ...)
distribution_student(n, df, ncp, random = FALSE, ...)
distribution_t(n, df, ncp, random = FALSE, ...)
distribution_student_t(n, df, ncp, random = FALSE, ...)
distribution_tweedie(n, xi = NULL, mu, phi, power = NULL, random = FALSE, ...)
distribution_uniform(n, min = 0, max = 1, random = FALSE, ...)
rnorm_perfect(n, mean = 0, sd = 1)
```

## Arguments

- type
Can be any of the names from base R's Distributions, like

`"cauchy"`

,`"pois"`

or`"beta"`

.- ...
Arguments passed to or from other methods.

- n
the number of observations

- random
Generate near-perfect or random (simple wrappers for the base R

`r*`

functions) distributions.- shape1
non-negative parameters of the Beta distribution.

- shape2
non-negative parameters of the Beta distribution.

- ncp
non-centrality parameter.

- size
number of trials (zero or more).

- prob
probability of success on each trial.

- location
location and scale parameters.

- scale
location and scale parameters.

- df
degrees of freedom (non-negative, but can be non-integer).

- shape
shape and scale parameters. Must be positive,

`scale`

strictly.- mean
vector of means.

- sd
vector of standard deviations.

- mu
the mean

- phi
Corresponding to

`glmmTMB`

's implementation of nbinom distribution, where`size=mu/phi`

.- lambda
vector of (non-negative) means.

- xi
For tweedie distributions, the value of

`xi`

such that the variance is`var(Y) = phi * mu^xi`

.- power
Alias for

`xi`

.- min
lower and upper limits of the distribution. Must be finite.

- max
lower and upper limits of the distribution. Must be finite.