weightedScores: Weighted Scores Method for Regression Models with Dependent Data

Has functions that handle the steps for the weighted scores method in Nikoloulopoulos, Joe and Chaganty (2011, Biostatistics, 12: 653-665) for binary (logistic and probit), Poisson and negative binomial regression, with dependent data. Two versions of negative binomial regression from Cameron and Trivedi (1998) are used. Let NB(tau,xi) be a parametrization with probability mass function f(y; tau, xi) = Gamma(tau + y) xi^y / [ Gamma(tau) y! (1 + xi)^( tau + y )], for y = 0, 1, 2, ... , tau > 0 , xi > 0, with mean mu = tau*xi = exp(beta^T x) and variance tau*xi*(1 + xi), where x is a vector of covariates. For NB1, the parameter gamma is defined so that tau=mu/gamma, xi=gamma; for NB2, the parameter gamma is defined so that tau=1/gamma, xi=mu*gamma. In NB1, the convolution parameter tau is a function of the covariate x and xi is constant; in NB2, the convolution parameter tau is constant and xi is a function of the covariate x.

Version: 0.9.1
Depends: R (≥ 2.0.0), mvtnorm, rootSolve
Published: 2014-11-07
Author: A. K. Nikoloulopoulos and H. Joe
Maintainer: Aristidis K. Nikoloulopoulos <A.Nikoloulopoulos at uea.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: weightedScores results


Reference manual: weightedScores.pdf
Package source: weightedScores_0.9.1.tar.gz
Windows binaries: r-devel: weightedScores_0.9.1.zip, r-release: weightedScores_0.9.1.zip, r-oldrel: weightedScores_0.9.1.zip
OS X Snow Leopard binaries: r-release: weightedScores_0.9.1.tgz, r-oldrel: weightedScores_0.9.1.tgz
OS X Mavericks binaries: r-release: weightedScores_0.9.1.tgz
Old sources: weightedScores archive