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KernelKnn: Kernel k Nearest Neighbors

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Version: 1.1.5
Depends: R (≥ 2.10.0)
Imports: Rcpp (≥ 0.12.5)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, covr, knitr, rmarkdown
Published: 2023-01-06
DOI: 10.32614/CRAN.package.KernelKnn
Author: Lampros Mouselimis ORCID iD [aut, cre], Matthew Parks [ctb] (Github Contributor)
Maintainer: Lampros Mouselimis <mouselimislampros at gmail.com>
BugReports: https://github.com/mlampros/KernelKnn/issues
License: MIT + file LICENSE
URL: https://github.com/mlampros/KernelKnn
NeedsCompilation: yes
SystemRequirements: libarmadillo: apt-get install -y libarmadillo-dev (deb), libblas: apt-get install -y libblas-dev (deb), liblapack: apt-get install -y liblapack-dev (deb), libarpack++2: apt-get install -y libarpack++2-dev (deb), gfortran: apt-get install -y gfortran (deb)
Citation: KernelKnn citation info
Materials: README NEWS
CRAN checks: KernelKnn results

Documentation:

Reference manual: KernelKnn.pdf
Vignettes: binary classification using the ionosphere data
Image classification of the MNIST and CIFAR-10 data using KernelKnn and HOG (histogram of oriented gradients)
Regression using the Housing data

Downloads:

Package source: KernelKnn_1.1.5.tar.gz
Windows binaries: r-devel: KernelKnn_1.1.5.zip, r-release: KernelKnn_1.1.5.zip, r-oldrel: KernelKnn_1.1.5.zip
macOS binaries: r-release (arm64): KernelKnn_1.1.5.tgz, r-oldrel (arm64): KernelKnn_1.1.5.tgz, r-release (x86_64): KernelKnn_1.1.5.tgz, r-oldrel (x86_64): KernelKnn_1.1.5.tgz
Old sources: KernelKnn archive

Reverse dependencies:

Reverse depends: elmNNRcpp
Reverse imports: demuxSNP, imbalance, nmslibR, RaSEn, scMultiSim
Reverse suggests: SuperLearner, superMICE

Linking:

Please use the canonical form https://CRAN.R-project.org/package=KernelKnn to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.