Package: hetcorFS
Type: Package
Title: Unsupervised Feature Selection using the Heterogeneous
        Correlation Matrix
Version: 1.0.1
Authors@R: c(person(given = "Cristina",
                      family = "Tortora",
                      role = c("aut", "cre", "fnd"),
                      email = "grikris1@gmail.com"),
               person(given = "Antonio",
                      family = "Punzo",
                      role = "aut"),
               person(given = "Shaam",
                      family = "Madhvani",
                      role = "aut"))
Description: 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 <doi: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).
License: GPL-2
Imports: polycor, dplyr, cluster, graphics,psych
Depends: R (>= 3.5.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2025-11-24 10:15:36 UTC; cristina
Author: Cristina Tortora [aut, cre, fnd],
  Antonio Punzo [aut],
  Shaam Madhvani [aut]
Maintainer: Cristina Tortora <grikris1@gmail.com>
Repository: CRAN
Date/Publication: 2025-11-24 13:30:09 UTC
Built: R 4.6.0; ; 2026-04-26 13:26:09 UTC; unix
