GBeta: Generalized Beta distribution

In gbeta: Generalized Beta and Beta Prime Distributions

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’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

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

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