# 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

 ```1 2 3 4 5 6 7``` ```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

 ```1 2 3 4``` ```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.