Package: dcsvm 0.0.1

dcsvm: Density Convoluted Support Vector Machines

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) <doi:10.1109/TIT.2022.3222767>.

Authors:Boxiang Wang [aut, cre], Le Zhou [aut], Yuwen Gu [aut], Hui Zou [aut]

dcsvm_0.0.1.tar.gz
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dcsvm_0.0.1.tgz(r-4.4-x86_64)dcsvm_0.0.1.tgz(r-4.4-arm64)dcsvm_0.0.1.tgz(r-4.3-x86_64)dcsvm_0.0.1.tgz(r-4.3-arm64)
dcsvm_0.0.1.tar.gz(r-4.5-noble)dcsvm_0.0.1.tar.gz(r-4.4-noble)
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dcsvm.pdf |dcsvm.html
dcsvm/json (API)

# Install 'dcsvm' in R:
install.packages('dcsvm', repos = c('https://boxiang-wang.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • colon - Simplified Gene Expression Data from Alon et al.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortran

1.00 score 3 exports 2 dependencies

Last updated 4 days agofrom:5a439b70d0. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 11 2025
R-4.5-win-x86_64OKJan 11 2025
R-4.5-linux-x86_64OKJan 11 2025
R-4.4-win-x86_64OKJan 11 2025
R-4.4-mac-x86_64OKJan 11 2025
R-4.4-mac-aarch64OKJan 11 2025
R-4.3-win-x86_64OKJan 11 2025
R-4.3-mac-x86_64OKJan 11 2025
R-4.3-mac-aarch64OKJan 11 2025

Exports:cv.dcsvmdcsvmprint.dcsvm

Dependencies:latticeMatrix