distribution  R Documentation 
Generate a sequence of nquantiles, i.e., a sample of size n
with a
nearperfect 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_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)
type 
Can be any of the names from base R's
Distributions, like 
... 
Arguments passed to or from other methods. 
n 
the number of observations 
random 
Generate nearperfect or random (simple wrappers for the base R

shape1 , shape2 
nonnegative parameters of the Beta distribution. 
ncp 
noncentrality 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 (nonnegative, but can be noninteger). 
shape 
Shape parameter. 
mean 
vector of means. 
sd 
vector of standard deviations. 
mu 
the mean 
phi 
Corresponding to 
lambda 
vector of (nonnegative) means. 
xi 
For tweedie distributions, the value of 
power 
Alias for 
min , max 
lower and upper limits of the distribution. Must be finite. 
When random = FALSE
, these function return q*(ppoints(n), ...)
.
library(bayestestR)
x < distribution(n = 10)
plot(density(x))
x < distribution(type = "gamma", n = 100, shape = 2)
plot(density(x))
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