distribution: Empirical Distributions

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

Empirical Distributions

Description

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, 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, scale	location and scale parameters.
df	degrees of freedom (non-negative, but can be non-integer).
shape	Shape parameter.
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, max	lower and upper limits of the distribution. Must be finite.

Details

When random = FALSE, these function return ⁠q*(ppoints(n), ...)⁠.

Examples

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

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

bayestestR documentation built on June 8, 2025, 10:18 a.m.