weightedScores-package: Weighted Scores Method for Regression Models with Dependent...

In weightedScores: Weighted Scores Method for Regression Models with Dependent Data

Weighted Scores Method for Regression Models with Dependent Data

Description

The weighted scores method and CL1 information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) and Nikoloulopoulos (2016, 2020).

Details

This package contains R functions to implement:

• The weighted scores method for regression models with dependent data and negative binomial (Nikoloulopoulos, Joe and Chaganty, 2011), GLM (Nikoloulopoulos, 2016) and ordinal probit/logistic (Nikoloulopoulos, 2020) margins.

• The composite likelihood information criteria for regression models with dependent data and ordinal probit/logistic margins (Nikoloulopoulos, 2016, 2020).

Author(s)

Aristidis K. Nikoloulopoulos.

References

Nikoloulopoulos, A.K., Joe, H. and Chaganty, N.R. (2011) Weighted scores method for regression models with dependent data. Biostatistics, 12, 653–665. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biostatistics/kxr005")}.

Nikoloulopoulos, A.K. (2016) Correlation structure and variable selection in generalized estimating equations via composite likelihood information criteria. Statistics in Medicine, 35, 2377–2390. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.6871")}.

Nikoloulopoulos, A.K. (2020) Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response. Journal of Statistical Computation and Simulation, 90, 2002–2022. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00949655.2020.1759602")}.

weightedScores documentation built on Aug. 23, 2026, 5:11 p.m.