The R Journal: article published in 2019, volume 11:2

HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions
Henrique Hoeltgebaum and Heather Battey , The R Journal (2019) 11:2, pages 370-379.

Abstract In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify as many of these explanations as is feasible. The standard practice, by contrast, is to report a single effective model for prediction. This paper illustrates the R implementation of the new ideas in the package HCmodelSets, using simple reproducible examples and real data. Results of some simulation experiments are also reported.

Received: 2019-03-28; online 2020-01-06, supplementary material, (1.3 KiB)
CRAN packages: HCmodelSets, glmnet, survival
CRAN Task Views implied by cited CRAN packages: Survival, ClinicalTrials, Econometrics, MachineLearning, SocialSciences

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

@article{RJ-2019-057,
  author = {Henrique Hoeltgebaum and Heather Battey},
  title = {{HCmodelSets: An R Package for Specifying Sets of Well-
          fitting Models in High Dimensions}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-057},
  url = {https://doi.org/10.32614/RJ-2019-057},
  pages = {370--379},
  volume = {11},
  number = {2}
}