LEEW: Log-Extended Exp-Weibull Distribution

In bistoonh/NewDists: The GOGaG and EEG family of distributions.

Description

These functions provide density and distribution function of the Log-Extended Exp-Weibull distribution due to Alizadeh et al. (2018) specified by the pdf

f(y;α,β,μ,σ)= \frac{\exp≤ft(\frac{y-μ}{σ}-{\rm e}^{\frac{y-μ}{σ}} \right)≤ft[1-\exp≤ft(-{\rm e}^{\frac{y-μ}{σ}}\right) \right]^{α-1} ≤ft\{α +(β-α)≤ft[1-\exp≤ft(-{\rm e}^{\frac{y-μ}{σ}}\right)\right]^β \right\}} {σ≤ft\{≤ft[1-\exp≤ft(-{\rm e}^{\frac{y-μ}{σ}}\right)\right]^α+1- ≤ft[1-\exp≤ft(-{\rm e}^{\frac{y-μ}{σ}}\right)\right]^β \right\}^2},

where α, β, σ > 0.

Usage

dLEEW(x, alpha, beta, mu = 0, sigma = 1, log = FALSE)
pLEEW(x, alpha, beta, mu = 0, sigma = 1, log = FALSE)

Arguments

x	Scaler or vector of values at which the pdf or cdf needs to be computed
alpha	The value of the first shape parameter. Must be positive and finite.
beta	The value of the second shape parameter. Must be positive and finite.
mu	Value of mean. Must be finite.
sigma	Value of standard deviations. Must be positive and finite.
log	Logical; if TRUE, probabilities p are given as log(p).

Value

An object of the same length as x, giving the pdf or cdf values computed at x.

Author(s)

Bistoon Hosseini, Mahmoud Afshari

References

Alizadeh, Morad, Mahmoud Afshari, Bistoon Hosseini, and Thiago G. Ramires. "Extended exp-G family of distributions: Properties, applications and simulation." Communications in Statistics-Simulation and Computation (2018): 1-16.

Examples

x = sort(rnorm(10, mean = -2 , sd = 1))
dLEEW(x, alpha = 0.5, beta = 1.2, mu = -2, sigma = 0.2, log = FALSE)
dLEEW(x, alpha = 0.5, beta = 1.2, mu = -2, sigma = 0.2, log = TRUE)
pLEEW(x, alpha = 0.5, beta = 1.2, mu = -2, sigma = 0.2)

bistoonh/NewDists documentation built on May 21, 2019, 11:09 a.m.