Empirical Distributions

Generate a sequence of n-quantiles, i.e., a sample of size n with a near-perfect distribution.

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_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.
lambda	vector of (non-negative) means.
xi	the value of \(\xi\) such that the variance is \(\mbox{var}[Y]=\phi\mu^{\xi}\)
mu	the mean
phi	the dispersion
power	a synonym for \(\xi\)
min	lower and upper limits of the distribution. Must be finite.
max	lower and upper limits of the distribution. Must be finite.

Examples

library(bayestestR)
x <- distribution(n = 10)
plot(density(x))

x <- distribution(type = "gamma", n = 100, shape = 2)
plot(density(x))