rmgammageo: The multivariate mixed gamma-geometric distributions (MMGG)

In camponsah/BivMixDist: Fit bivariate continous and discrete distributions as well as provide EM algorithm for discrete Pareto

Description

Random sample generating function for multivariate mixed gamma-geometric distribution with parameters α_1, α_1 >0, β_1, β_1 > 0, p_1, P_2 \in (0,1) and -1≤q θ \frac{1+\min(p_1,p_2)}{1+\min(p_1,p_2)}.

Usage

rmgammageo(n, alpha, beta, prob, theta, log.p = FALSE)

Arguments

n	sample size.
alpha	numeric vector of parameters which must be greater than 0.
beta	numeric vector of parameters which must be greater than 0.
prob	numeric vector parameters between 0 and 1.
theta	numeric parameter between -1 and (1+min(p))/(1-min(p)).
log.p	logical; if TRUE, densities are given as log(den).

Details

rmgammageo is the density function.

Value

vector of random sample.

References

Amponsah, C. K. and Kozubowski, T.J., (2022). Inprint

Examples

data.df <- rmgammageo(20, alpha=c(0.5, 1.5), beta=c(20, 50), prob=c(0.6, 0.4), theta= -0.2)
data.df

camponsah/BivMixDist documentation built on Nov. 15, 2021, 3:11 a.m.