Springer: An R package for bi-level variable selection of high-dimensional longitudinal data - Zhou, Fei; Liu, Yuwen; Ren, Jie; Wang, Weiqun; Wu, Cen (Frontiers in Genetics, 2023)
In high-dimensional data analysis, the bi-level (or sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary and survival responses in literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function (QIF) based penalization method in gene-environment interaction studies. This article introduces "springer", an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, the R package "springer" has also implemented the generalized estimating equations (GEE) based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this software article, we have systematically introduced the longitudinal penalization methods implemented in package "springer". We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. The R package "springer" is available at \url{https://cran.r-project.org/package=springer}.