Package: sparseSVM 1.1-7

sparseSVM: Solution Paths of Sparse High-Dimensional Support Vector Machine with Lasso or Elastic-Net Regularization

Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Authors:Congrui Yi [aut, cre], Yaohui Zeng [aut]

sparseSVM_1.1-7.tar.gz
sparseSVM_1.1-7.zip(r-4.7)sparseSVM_1.1-7.zip(r-4.6)sparseSVM_1.1-7.zip(r-4.5)
sparseSVM_1.1-7.tgz(r-4.6-x86_64)sparseSVM_1.1-7.tgz(r-4.6-arm64)sparseSVM_1.1-7.tgz(r-4.5-x86_64)sparseSVM_1.1-7.tgz(r-4.5-arm64)
sparseSVM_1.1-7.tar.gz(r-4.7-arm64)sparseSVM_1.1-7.tar.gz(r-4.7-x86_64)sparseSVM_1.1-7.tar.gz(r-4.6-arm64)sparseSVM_1.1-7.tar.gz(r-4.6-x86_64)
sparseSVM_1.1-7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
sparseSVM/json (API)

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

Bug tracker:https://github.com/cy-dev/sparsesvm/issues

On CRAN:

Conda:

elasticnethigh-dimensionallassomachine-learning-algorithmsregularization-pathssvm

4.35 score 6 stars 25 scripts 176 downloads 1 mentions 2 exports 0 dependencies

Last updated from:9fe031c420. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK96
linux-devel-x86_64OK107
source / vignettesOK142
linux-release-arm64OK107
linux-release-x86_64OK98
macos-release-arm64OK107
macos-release-x86_64OK212
macos-oldrel-arm64OK152
macos-oldrel-x86_64OK189
windows-develOK78
windows-releaseOK73
windows-oldrelOK79
wasm-releaseOK81

Exports:cv.sparseSVMsparseSVM

Dependencies: