Description Usage Arguments Examples
Generate a sequence of nquantiles, i.e., a sample of size n
with a
nearperfect distribution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39  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 
nonnegative parameters of the Beta distribution. 
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 
location and scale parameters. 
scale 
location and scale parameters. 
df 
degrees of freedom (nonnegative, but can be noninteger). 
shape 
shape and scale parameters. Must be positive,

mean 
vector of means. 
sd 
vector of standard deviations. 
mu 
the mean 
phi 
Corresponding to 
lambda 
vector of (nonnegative) means. 
xi 
the value of xi such that the variance is var(Y) = phi * mu^xi 
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. 
1 2 3 4 5 6  library(bayestestR)
x < distribution(n = 10)
plot(density(x))
x < distribution(type = "gamma", n = 100, shape = 2)
plot(density(x))

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