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: | 1.1.0 |
Depends: | R (≥ 2.10.0) |
Imports: | Rcpp (≥ 0.12.5) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | testthat, covr, knitr, rmarkdown |
Published: | 2019-11-29 |
Author: | Lampros Mouselimis |
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) |
Materials: | README NEWS |
CRAN checks: | KernelKnn results |
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 |
Package source: | KernelKnn_1.1.0.tar.gz |
Windows binaries: | r-devel: KernelKnn_1.1.0.zip, r-release: KernelKnn_1.1.0.zip, r-oldrel: KernelKnn_1.1.0.zip |
macOS binaries: | r-release: KernelKnn_1.1.0.tgz, r-oldrel: KernelKnn_1.1.0.tgz |
Old sources: | KernelKnn archive |
Reverse depends: | elmNNRcpp |
Reverse imports: | imbalance, nmslibR |
Reverse suggests: | SuperLearner |
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