Title: Generalised Estimating Equations (GEE/WGEE) using 'Armadillo'
Authors@R: c(person("Jianxin", "Pan", email = "Jianxin.Pan@manchester.ac.uk",
Description: Fit joint mean-covariance models for longitudinal data within the
framework of (weighted) generalised estimating equations (GEE/WGEE). The
models and their components are represented using S4 classes and methods.
The core computational algorithms are implemented using the 'Armadillo' C++
library for numerical linear algebra and 'RcppArmadillo' glue.
Imports: Formula, methods, Rcpp (>= 0.12.4)

Title: Bias-Corrected GEE for Cluster Randomized Trials
person(given = "Paul", family = "Rathouz", role ="aut",
person(given = "John", family = "Preisser", role ="aut",
Description: Population-averaged models have been increasingly used in the design and analysis of
cluster randomized trials (CRTs). To facilitate the applications of population-averaged
models in CRTs, the package implements the generalized estimating equations (GEE) and
matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal
mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the
general GEE/MAEE approach, the package also implements a fast cluster-period GEE method
specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives
a simple and efficient estimating equations approach based on the cluster-period means to
estimate the intervention effects as well as correlation parameters. In addition, the package
also provides functions for generating correlated binary data with specific mean vector and
correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or
the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.
Packaged: 2020-11-07 01:39:08 UTC; hengshiyu

general GEE/MAEE approach, the package also implements a fast cluster-period GEE method by
Li et al. (2021) <doi:10.1093/biostatistics/kxaa056>
Packaged: 2021-04-15 00:55:30 UTC; hengshiyu