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weightedScores (version 0.9.5.1)

Weighted Scores Method for Regression Models with Dependent Data

Description

Has functions to implement the weighted scores method and CL1 information criteria as an intermediate step for variable/correlation selection for longitudinal categorical and count data in Nikoloulopoulos, Joe and Chaganty (2011, Biostatistics, 12: 653-665) and Nikoloulopoulos (2015a,2015b).

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Version

Install

install.packages('weightedScores')

Monthly Downloads

120

Version

0.9.5.1

License

GPL (>= 2)

Maintainer

Aristidis Nikoloulopoulos

Last Published

October 22nd, 2015

Functions in weightedScores (0.9.5.1)

Exchangeable (positive) multivariate normal

APPROXIMATION OF BIVARIATE STANDARD NORMAL DISTRIBUTION

Maximum Likelihood for Ordinal Model

INVERSE GODAMBE MATRIX

CL1 INFORMATION CRITERIA

Derivatives of Multivariate Normal Rectangle Probabilities

SOLVING THE WEIGHTED SCORES EQUATIONS WITH INPUTS OF THE WEIGHT MATRICES AND THE DATA

DENSITY AND CDF OF THE UNIVARIATE MARGINAL DISTRIBUTION

OPTIMIZATION ROUTINE FOR BIVARIATE COMPOSITE LIKELIHOOD FOR MVN COPULA

NEGATIVE LOG-LIKELIHOOD ASSUMING INDEPEDENCE WITHIN CLUSTERS

WEIGHT MATRICES FOR THE ESTIMATING EQUATIONS

Rheumatoid Arthritis Clinical Trial

MVN Rectangle Probabilities

THE WEIGHTED SCORES EQUATIONS WITH INPUTS OF THE WEIGHT MATRICES AND THE DATA

COVARIANCE MATRIX OF THE UNIVARIATE SCORES

BIVARIATE COMPOSITE LIKELIHOOD FOR MULTIVARIATE NORMAL COPULA WITH CATEGORICAL AND COUNT REGRESSION

THE WEIGHTED SCORES METHOD WITH INPUTS OF THE DATA

INDEPENDENT ESTIMATING EQUATIONS FOR BINARY AND COUNT REGRESSION