moors_lss: Measures of location, scale, and shape based on quantile...
In sglg: Fitting Semi-Parametric Generalized log-Gamma Regression Models

moors_lss

R Documentation

Measures of location, scale, and shape based on quantile measures for a generalized log-gamma distribution

Description

moors_lss is used to obtain the median, the half interquartile range and the quantile coefficient of skewness and kurtosis for a generalized log-gamma distribution.

Usage

moors_lss(mu = 0, sigma = 1, lambda = 1)

Arguments

`mu`	numeric, represents the location parameter of a generalized log-gamma distribution. Default value is 0.
`sigma`	numeric, represents the scale parameter of a generalized log-gamma distribution. Default value is 1.
`lambda`	numeric, represents the shape parameter of a generalized log-gamma distribution. Default value is 1.

Author(s)

Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>

References

Carlos Alberto Cardozo Delgado, Semi-parametric generalized log-gamma regression models. Ph. D. thesis. Sao Paulo University.

J. J. A. Moors (1988), A quantile alternative for kurtosis. The Statistician.

Examples

moors_lss(mu = 0,sigma = 1,lambda = -1)    # Extreme value type I distribution, maximum case.
moors_lss(mu = 0,sigma = 1,lambda = 1)     # Extreme value type I distribution, minimum case.
moors_lss(mu = 0,sigma = 1,lambda = 0.05) # Standard normal distribution.

sglg documentation built on Nov. 28, 2025, 1:06 a.m.