Package: gldist
Version: 2160.2
Revision: 5308
Title: An Asymmetry-Steepness Parameterization of the Generalized
Lambda Distribution.
Date: 2012-04-02
Author: Yohan Chalabi and Diethelm Wuertz
Depends: R (>= 2.14.0)
Suggests: RUnit
Maintainer: Yohan Chalabi <chalabi@phys.ethz.ch>
License: GPL-2 | GPL-3
Description: The generalized lambda distribution (GLD) is a versatile
distribution that can accommodate a wide range of shapes,
including fat-tailed and asymmetric distributions. This package
implements a more intuitive parameterization of the GLD that
expresses the location and scale parameters directly as the
median and inter-quartile range of the distribution. The
remaining two shape parameters characterize the asymmetry and
steepness of the distribution respectively. The fitting of the
GLD to empirical data can be reduced to a two-parameter
estimation problem where the location and scale parameters are
estimated by their robust sample estimators. Moreover, the
parameterization can be used to compare data sets in a
convenient asymmetry and steepness shape plot. The underline C
routines are written such that compilers that support
vectorized mathematical operations can automatically vectorize
the most time consuming loops (tested with icc 12.1.0). The
package includes the usual distribution functions, fitting
routines and a shape plot function.
Packaged: 2012-04-02 14:33:12 UTC; yohan
Repository: CRAN
Date/Publication: 2012-04-02 17:59:04

Title: Generalized Linear Density Ratio Models
person("Michael", "Wurm", email = "wurm@wisc.edu", role = c("aut", "cre")),
person("Paul", "Rathouz", email = "rathouz@biostat.wisc.edu", role = "aut"))
Description: Fits a generalized linear density ratio model (GLDRM).
A GLDRM is a semiparametric generalized linear model.
In contrast to a GLM, which assumes a particular exponential family distribution,
the GLDRM uses a semiparametric likelihood to estimate the reference distribution.
The reference distribution may be any discrete, continuous, or mixed exponential
family distribution. The model parameters, which include both the regression
coefficients and the cdf of the unspecified reference distribution, are estimated
by maximizing a semiparametric likelihood. Regression coefficients are estimated
with no loss of efficiency, i.e. the asymptotic variance is the same as if the
true exponential family distribution were known.