dmgammageo: 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

Density 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

dmgammageo(data, alpha, beta, prob, theta, log.p = FALSE)

Arguments

data	bivariate vector (X,N) observations from BGG model.
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

dmgammageo is the density function.

Value

vector of densities.

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

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