gbeta (version 0.1.0)

GBeta: Generalized Beta distribution

Description

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

Usage

dgbeta(u, c, d, kappa, tau, log = FALSE)
pgbeta(q, c, d, kappa, tau)
rgbeta(n, c, d, kappa, tau, method = "mixture")
qgbeta(p, c, d, kappa, tau)

Arguments

u

numeric vector

c, d, kappa, tau

parameters; they must be strictly positive numbers, except kappa which can take any value

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

• Marwa Hamza & Pierre Vallois. On Kummer<U+2019>s distributions of type two and generalized Beta distributions. Statistics & Probability Letters 118 (2016), pp. 60-69. <doi:10.1016/j.spl.2016.03.014>

• James J. Chen & Melvin R. Novick. Bayesian Analysis for Binomial Models with Generalized Beta Prior Distributions. Journal of Educational Statistics 9, No. 2 (1984), pp. 163-175. <doi:10.3102/10769986009002163>

Examples

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

# }