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

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

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