VariableScreening: High-Dimensional Screening for Semiparametric Longitudinal Regression

Implements a screening procedure proposed by Wanghuan Chu, Runze Li and Matthew Reimherr (2016) <DOI:10.1214/16-AOAS912> for varying coefficient longitudinal models with ultra-high dimensional predictors . The effect of each predictor is allowed to vary over time, approximated by a low-dimensional B-spline. Within-subject correlation is handled using a generalized estimation equation approach with structure specified by the user. Variance is allowed to change over time, also approximated by a B-spline.

Author
Runze Li [aut], Wanghuan Chu [aut], Liying Huang [aut, cre], John Dziak [aut]
Date of publication
2016-07-28 17:28:27
Maintainer
Liying Huang <lxh37@PSU.EDU>
License
GPL (>= 2)
Version
0.1.1

View on CRAN

Man pages

screenlong
Perform high-dimensional screening for semiparametric...
simulateLD
Simulate a dataset for testing the performance of screenlong

Files in this package

VariableScreening
VariableScreening/NAMESPACE
VariableScreening/R
VariableScreening/R/simulateLD.R
VariableScreening/R/screenlong.R
VariableScreening/MD5
VariableScreening/DESCRIPTION
VariableScreening/man
VariableScreening/man/screenlong.Rd
VariableScreening/man/simulateLD.Rd