The R Journal: article published in 2017, volume 9:1

PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates
Gul Inan and Lan Wang , The R Journal (2017) 9:1, pages 393-402.

Abstract We introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) procedure proposed by Wang et al. (2012) to analyze longitudinal data with a large number of covariates. The PGEE package includes three main functions: CVfit, PGEE, and MGEE. The CVfit function computes the cross-validated tuning parameter for penalized generalized estimating equations. The function PGEE performs simultaneous estimation and variable selection for longitudinal data with high-dimensional covariates; whereas the function MGEE fits unpenalized GEE to the data for comparison. The R package PGEE is illustrated using a yeast cell-cycle gene expression data set.

Received: 2016-09-15; online 2017-06-08
CRAN packages: gee, geepack, PGEE, MASS, mvtnorm, ncvreg, penalized, glmnet, rqPen
CRAN Task Views implied by cited CRAN packages: MachineLearning, SocialSciences, Distributions, Econometrics, Multivariate, Survival, Environmetrics, Finance, NumericalMathematics, Psychometrics, Robust

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-030,
  author = {Gul Inan and Lan Wang},
  title = {{PGEE: An R Package for Analysis of Longitudinal Data with
          High-Dimensional Covariates}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-030},
  url = {https://doi.org/10.32614/RJ-2017-030},
  pages = {393--402},
  volume = {9},
  number = {1}
}