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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "dcsvm" in publications use:'
type: software
license: GPL-2.0-only
title: 'dcsvm: Density Convoluted Support Vector Machines'
version: 0.0.1
doi: 10.1109/TIT.2022.3222767
identifiers:
- type: doi
  value: 10.32614/CRAN.package.dcsvm
abstract: Implements an efficient algorithm for solving sparse-penalized support vector
  machines with kernel density convolution. This package is designed for high-dimensional
  classification tasks, supporting lasso (L1) and elastic-net penalties for sparse
  feature selection and providing options for tuning kernel bandwidth and penalty
  weights. The 'dcsvm' is applicable to fields such as bioinformatics, image analysis,
  and text classification, where high-dimensional data commonly arise. Learn more
  about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) <https://doi.org/10.1109/TIT.2022.3222767>.
authors:
- family-names: Wang
  given-names: Boxiang
  email: boxiang-wang@uiowa.edu
- family-names: Zhou
  given-names: Le
- family-names: Gu
  given-names: Yuwen
- family-names: Zou
  given-names: Hui
preferred-citation:
  type: article
  title: Density-convoluted support vector machines for high-dimensional classification
  authors:
  - family-names: Wang
    given-names: Boxiang
    email: boxiang-wang@uiowa.edu
  - family-names: Zhou
    given-names: Le
  - family-names: Gu
    given-names: Yuwen
  - family-names: Zou
    given-names: Hui
  journal: IEEE Transactions on Information Theory
  year: '2023'
  volume: '69'
  issue: '4'
  publisher:
    name: IEEE
  doi: 10.1109/TIT.2022.3222767
  start: '2523'
  end: '2536'
repository: https://boxiang-wang.r-universe.dev
commit: 5a439b70d0dbc4ef00bb398fa774552a0c49b049
date-released: '2025-01-08'
contact:
- family-names: Wang
  given-names: Boxiang
  email: boxiang-wang@uiowa.edu