GBetaP: Generalized Beta prime distribution

In gbeta: Generalized Beta and Beta Prime Distributions

Description

Density, distribution function, quantile function, and random generation for the generalized Beta prime distribution.

Usage

dgbetap(x, c, d, kappa, tau, scale = 1, log = FALSE)

pgbetap(q, c, d, kappa, tau, scale = 1)

rgbetap(n, c, d, kappa, tau, scale = 1, method = "mixture")

qgbetap(p, c, d, kappa, tau, scale = 1)

Arguments

x	numeric vector
c, d, kappa, tau	parameters; they must be strictly positive numbers, except kappa which can take any value
scale	scale parameter, a strictly positive number
log	logical, whether to return the log-density
q	numeric vector of quantiles
n	positive integer, the desired number of simulations
method	the method of random generation, "mixture" or "arou"; only a positive kappa is allowed for the "mixture" method, but this method is faster
p	numeric vector of probabilities

References

• Stéphane Laurent. Some Poisson mixtures distributions with a hyperscale parameter. Brazilian Journal of Probability and Statistics 26, No. 3 (2012), pp. 265-278. <doi:10.1214/11-BJPS139>

• Myriam Chabot. Sur l’estimation du rapport de deux paramètres d’intensité poissonniens et l’estimation par fonctions de masse prédictives. Master thesis. Université de Scherbrooke, 2016.

Examples

library(gbeta)
curve(dgbetap(x, 4, 12, 10, 0.01), to = 10, axes = FALSE, lwd = 2)
axis(1)
  

gbeta documentation built on Nov. 19, 2020, 9:07 a.m.