gbeta (version 0.1.0)

GBetaP: Generalized Beta prime distribution

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

• St<U+00E9>phane 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<U+2019>estimation du rapport de deux param<U+00E8>tres d<U+2019>intensit<U+00E9> poissonniens et l<U+2019>estimation par fonctions de masse pr<U+00E9>dictives. Master thesis. Universit<U+00E9> de Scherbrooke, 2016.

Examples

# NOT RUN {
library(gbeta)
curve(dgbetap(x, 4, 12, 10, 0.01), to = 10, axes = FALSE, lwd = 2)
axis(1)
  
# }