The R Journal: article published in 2018, volume 10:2

rFSA: An R Package for Finding Best Subsets and Interactions
Joshua Lambert, Liyu Gong, Corrine F. Elliott, Katherine Thompson and Arnold Stromberg , The R Journal (2018) 10:2, pages 295-308.

Abstract Herein we present the R package rFSA, which implements an algorithm for improved variable selection. The algorithm searches a data space for models of a user-specified form that are statistically optimal under a measure of model quality. Many iterations afford a set of feasible solutions (or candidate models) that the researcher can evaluate for relevance to his or her questions of interest. The algorithm can be used to formulate new or to improve upon existing models in bioinformatics, health care, and myriad other fields in which the volume of available data has outstripped researchers’ practical and computational ability to explore larger subsets or higher-order interaction terms. The package accommodates linear and generalized linear models, as well as a variety of criterion functions such as Allen’s PRESS and AIC. New modeling strategies and criterion functions can be adapted easily to work with rFSA.

Received: 2018-03-02; online 2018-12-08, supplementary material, (2.3 KiB)
CRAN packages: rFSA, leaps, glmulti, glmnet, hierNet, hashmap, geepack, devtools
CRAN Task Views implied by cited CRAN packages: SocialSciences, ChemPhys, Econometrics, MachineLearning, Survival

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-059,
  author = {Joshua Lambert and Liyu Gong and Corrine F. Elliott and
          Katherine Thompson and Arnold Stromberg},
  title = {{rFSA: An R Package for Finding Best Subsets and Interactions}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-059},
  url = {https://doi.org/10.32614/RJ-2018-059},
  pages = {295--308},
  volume = {10},
  number = {2}
}