distribution.R
``````#' Empirical Distributions
#'
#' Generate a sequence of n-quantiles, i.e., a sample of size `n` with a
#' near-perfect distribution.
#'
#' @param type Can be any of the names from base R's
#'   [Distributions][stats::Distributions], like `"cauchy"`, `"pois"` or
#'   `"beta"`.
#' @param random Generate near-perfect or random (simple wrappers for the base R
#'   `r*` functions) distributions.
#' @param ... Arguments passed to or from other methods.
#' @inheritParams tweedie::rtweedie
#'
#' @examples
#' library(bayestestR)
#' x <- distribution(n = 10)
#' plot(density(x))
#'
#' x <- distribution(type = "gamma", n = 100, shape = 2)
#' plot(density(x))
#' @export
distribution <- function(type = "normal", ...) {
basr_r_distributions <- c(
"beta", "binom", "binomial", "cauchy", "chisq", "chisquared", "exp", "f",
"gamma", "geom", "hyper", "lnorm", "multinom", "nbinom", "normal", "gaussian",
"pois", "poisson", "student", "t", "student_t", "unif", "uniform", "weibull"
)

switch(match.arg(arg = type, choices = basr_r_distributions),
"beta" = distribution_beta(...),
"binom" = ,
"binomial" = distribution_binomial(...),
"cauchy" = distribution_cauchy(...),
"chisq" = ,
"chisquared" = distribution_chisquared(...),
"gamma" = distribution_gamma(...),
"gaussian" = ,
"normal" = distribution_normal(...),
"nbinom" = distribution_nbinom(...),
"poisson" = distribution_poisson(...),
"t" = ,
"student" = ,
"student_t" = distribution_student(...),
"uniform" = distribution_uniform(...),
distribution_custom(type = type, ...)
)
}

#' @rdname distribution
#' @inheritParams distribution
#' @export
distribution_custom <- function(n, type = "norm", ..., random = FALSE) {
if (random) {
f <- match.fun(paste0("r", type))
f(n, ...)
} else {
f <- match.fun(paste0("q", type))
f(seq(1 / n, 1 - 1 / n, length.out = n), ...)
}
}

#' @rdname distribution
#' @inheritParams stats::rbeta
#' @export
distribution_beta <- function(n, shape1, shape2, ncp = 0, random = FALSE, ...) {
if (random) {
stats::rbeta(n, shape1, shape2, ncp = ncp)
} else {
stats::qbeta(seq(1 / n, 1 - 1 / n, length.out = n), shape1, shape2, ncp = ncp, ...)
}
}

#' @rdname distribution
#' @inheritParams stats::rbinom
#' @export
distribution_binomial <- function(n, size = 1, prob = 0.5, random = FALSE, ...) {
if (random) {
stats::rbinom(n, size, prob)
} else {
stats::qbinom(seq(1 / n, 1 - 1 / n, length.out = n), size, prob, ...)
}
}

#' @rdname distribution
#' @export
distribution_binom <- distribution_binomial

#' @rdname distribution
#' @inheritParams stats::rcauchy
#' @export
distribution_cauchy <- function(n, location = 0, scale = 1, random = FALSE, ...) {
if (random) {
stats::rcauchy(n, location, scale)
} else {
stats::qcauchy(seq(1 / n, 1 - 1 / n, length.out = n), location, scale, ...)
}
}

#' @rdname distribution
#' @inheritParams stats::rchisq
#' @export
distribution_chisquared <- function(n, df, ncp = 0, random = FALSE, ...) {
if (random) {
stats::rchisq(n, df, ncp)
} else {
stats::qchisq(seq(1 / n, 1 - 1 / n, length.out = n), df, ncp, ...)
}
}

#' @rdname distribution
#' @export
distribution_chisq <- distribution_chisquared

#' @rdname distribution
#' @inheritParams stats::rgamma
#' @export
distribution_gamma <- function(n, shape, scale = 1, random = FALSE, ...) {
if (random) {
stats::rgamma(n = n, shape = shape, scale = scale)
} else {
stats::qgamma(p = seq(1 / n, 1 - 1 / n, length.out = n), shape = shape, scale = scale)
}
}

#' @rdname distribution
#' @inheritParams stats::rnorm
#' @export
distribution_mixture_normal <- function(n, mean = c(-3, 3), sd = 1, random = FALSE, ...) {
n <- round(n / length(mean))
sd <- c(sd)
if (length(sd) != length(mean)) {
sd <- rep(sd, length.out = length(mean))
}

x <- c()
for (i in 1:length(mean)) {
x <- c(x, distribution_normal(n = n, mean = mean[i], sd = sd[i], random = random))
}
x
}

#' @rdname distribution
#' @inheritParams stats::rnorm
#' @export
distribution_normal <- function(n, mean = 0, sd = 1, random = FALSE, ...) {
if (random) {
stats::rnorm(n, mean, sd)
} else {
stats::qnorm(seq(1 / n, 1 - 1 / n, length.out = n), mean, sd, ...)
}
}

#' @rdname distribution
#' @export
distribution_gaussian <- distribution_normal

#' @rdname distribution
#' @inheritParams stats::rnbinom
#' @param phi Corresponding to `glmmTMB`'s implementation of nbinom
#'   distribution, where `size=mu/phi`.
#' @export
distribution_nbinom <- function(n, size, prob, mu, phi, random = FALSE, ...) {
if (missing(size)) {
size <- mu / phi
}

if (random) {
stats::rnbinom(n, size, prob, mu)
} else {
stats::qnbinom(seq(1 / n, 1 - 1 / n, length.out = n), size, prob, mu, ...)
}
}

#' @rdname distribution
#' @inheritParams stats::rpois
#' @export
distribution_poisson <- function(n, lambda = 1, random = FALSE, ...) {
if (random) {
stats::rpois(n, lambda)
} else {
stats::qpois(seq(1 / n, 1 - 1 / n, length.out = n), lambda, ...)
}
}

#' @rdname distribution
#' @inheritParams stats::rt
#' @export
distribution_student <- function(n, df, ncp, random = FALSE, ...) {
if (random) {
stats::rt(n, df, ncp)
} else {
stats::qt(seq(1 / n, 1 - 1 / n, length.out = n), df, ncp, ...)
}
}

#' @rdname distribution
#' @export
distribution_t <- distribution_student

#' @rdname distribution
#' @export
distribution_student_t <- distribution_student

#' @rdname distribution
#' @inheritParams tweedie::rtweedie
#' @export
distribution_tweedie <- function(n,
xi = NULL,
mu,
phi,
power = NULL,
random = FALSE,
...) {
insight::check_if_installed("tweedie")

if (random) {
tweedie::rtweedie(
n = n,
xi = xi,
mu = mu,
phi = phi,
power = power
)
} else {
tweedie::qtweedie(
p = seq(1 / n, 1 - 1 / n, length.out = n),
xi = xi,
mu = mu,
phi = phi,
power = power
)
}
}

#' @rdname distribution
#' @inheritParams stats::runif
#' @export
distribution_uniform <- function(n, min = 0, max = 1, random = FALSE, ...) {
if (random) {
stats::runif(n, min, max)
} else {
stats::qunif(seq(1 / n, 1 - 1 / n, length.out = n), min, max, ...)
}
}

#' @rdname distribution
#' @inheritParams stats::rnorm
#' @export
rnorm_perfect <- function(n, mean = 0, sd = 1) {
.Deprecated("distribution_normal")
stats::qnorm(seq(1 / n, 1 - 1 / n, length.out = n), mean, sd)
}
``````