# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "hetcorFS" in publications use:'
type: software
license: GPL-2.0-only
title: 'hetcorFS: Unsupervised Feature Selection using the Heterogeneous Correlation
  Matrix'
version: 1.0.1
doi: 10.32614/CRAN.package.hetcorFS
abstract: 'Unsupervised multivariate filter feature selection using the UFS-rHCM or
  UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM
  consists of Pearson''s correlations between numerical features, polyserial correlations
  between numerical and ordinal features, and polychoric correlations between ordinal
  features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type
  feature selection techniques using the heterogeneous correlation matrix." International
  Statistical Review <https://doi.org/10.1111/insr.70016>. This work was supported
  by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian
  Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R
  (CUP: E53D23005950006), as part of ‘The SMILE Project: Statistical Modelling and
  Inference to Live the Environment’, funded by the European Union – Next Generation
  EU (Punzo).'
authors:
- family-names: Tortora
  given-names: Cristina
  email: grikris1@gmail.com
- family-names: Punzo
  given-names: Antonio
- family-names: Madhvani
  given-names: Shaam
repository: https://cran.r-universe.dev
commit: be3f598431f6e86edfbe05f70c76e27ab22df2c5
date-released: '2025-11-24'
contact:
- family-names: Tortora
  given-names: Cristina
  email: grikris1@gmail.com