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Activity Number: 397
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #311589
Title: glmmplus: An R Package for Messy Longitudinal Data
Author(s): Ben Ogorek*+ and Caitlin Hogan
Companies: Google and Google
Keywords: imputation ; random effects ; R ; longitudinal ; variable selection ; missing data
Abstract:

In modeling tasks involving large longitudinal data sets, there is often the need for random effects, grouped predictor terms, missing data forgiveness, nonlinear link functions, and variable selection capabilities. Many existing R packages focus on one of these problems, but the separate sets of functionality do not always integrate seamlessly. The glmmplus package addresses this problem by offering a wrapper to trusted packages such as mice and lme4, and adding new functionality such as Fast False Selection Rate (FSR) control for both forward and backward selection. The result is a la carte functionality to the user for messy longitudinal data. An analysis is presented from the National Longitudinal Survey.


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