Package: kerndwd 2.0.3

kerndwd: Distance Weighted Discrimination (DWD) and Kernel Methods

A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

Authors:Boxiang Wang <[email protected]>, Hui Zou <[email protected]>

kerndwd_2.0.3.tar.gz
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kerndwd_2.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
kerndwd/json (API)

# Install 'kerndwd' in R:
install.packages('kerndwd', repos = c('https://boxiang-wang.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • BUPA - BUPA's liver disorders data

On CRAN:

Conda:

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

openblasfortran

1.18 score 15 scripts 242 downloads 14 exports 0 dependencies

Last updated from:19de439689. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK111
linux-devel-x86_64OK109
source / vignettesOK122
linux-release-arm64OK100
linux-release-x86_64OK91
macos-release-arm64OK77
macos-release-x86_64OK204
macos-oldrel-arm64OK77
macos-oldrel-x86_64OK222
windows-develOK88
windows-releaseOK89
windows-oldrelOK69
wasm-releaseOK74

Exports:anovadotbesseldotcv.kerndwdkerndwdlaplacedotplot.cv.kerndwdplot.kerndwdpolydotpredict.kerndwdrbfdotsigestsplinedottunedwdvanilladot

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Kernel Distance Weighted Discriminationkerndwd-package
BUPA's liver disorders dataBUPA
cross-validationcv.kerndwd
solve Linear DWD and Kernel DWDkerndwd
Kernel Functionsanovadot anovakernel-class besseldot besselkernel-class dots kern laplacedot laplacekernel-class polydot polykernel-class rbfdot rbfkernel-class sigest splinedot splinekernel-class vanilladot vanillakernel-class
plot the cross-validation curveplot.cv.kerndwd
plot coefficientsplot.kerndwd
predict class labels for new observationspredict.kerndwd
fast tune procedure for DWDtunedwd