# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "hetNSC" in publications use:' type: software license: GPL-2.0-only title: 'hetNSC: Heteroscedastic Nearest Shrunken Centroid Classification' version: 0.1.0 abstract: Nearest shrunken centroid (NSC) classifiers for high-dimensional, low-sample-size data such as gene expression. Implements the original homoscedastic NSC with soft thresholding (Prediction Analysis of Microarrays; Tibshirani et al. 2002 ), its hard- and order-thresholding versions (Sahtout, Wang and Ghimire 2024 ), and the heteroscedastic extension of Sahtout and Wang that uses class-specific variances in both the test statistic and the discriminant function (methods STh3, HTh3 and OTh3). The thresholding parameter is selected by a refined ("deep") cross-validation search. Also provides the simulation design, per-class performance metrics and the relative-heterogeneity diagnostic used in the paper. authors: - family-names: Wang given-names: Haiyan email: hwang@ksu.edu - family-names: Sahtout given-names: Mohammad Omar preferred-citation: type: article title: 'Heteroscedastic nearest shrunken centroid classification: Methods and applications to high-dimensional data' authors: - family-names: Sahtout given-names: Mohammad Omar - family-names: Wang given-names: Haiyan email: hwang@ksu.edu journal: Statistical Applications in Genetics and Molecular Biology year: '2026' notes: Accepted repository: https://cran.r-universe.dev commit: 4f40de6124a32cad636fff3c87cdf40067e7d0a8 date-released: '2026-11-07' contact: - family-names: Wang given-names: Haiyan email: hwang@ksu.edu references: - type: article title: Different thresholding methods on nearest shrunken centroid algorithm authors: - family-names: Sahtout given-names: Mohammad Omar - family-names: Wang given-names: Haiyan - family-names: Ghimire given-names: S. journal: Communications in Statistics - Simulation and Computation year: '2024' volume: '53' issue: '3' doi: 10.1080/03610918.2022.2047201 start: '1444' end: '1460'