Package: KernelKnn 1.1.5

Lampros Mouselimis

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.

Authors:Lampros Mouselimis [aut, cre], Matthew Parks [ctb]

KernelKnn_1.1.5.tar.gz
KernelKnn_1.1.5.tar.gz(r-4.5-noble)KernelKnn_1.1.5.tar.gz(r-4.4-noble)
KernelKnn_1.1.5.tgz(r-4.4-emscripten)KernelKnn_1.1.5.tgz(r-4.3-emscripten)
KernelKnn.pdf |KernelKnn.html
KernelKnn/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/mlampros/kernelknn/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • Boston - Boston Housing Data
  • ionosphere - Johns Hopkins University Ionosphere database

openblascppopenmp

7.10 score 13 packages 54 scripts 13k downloads 3 mentions 5 exports 2 dependencies

Last updated 2 years agofrom:1ec0ddd0c1. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 27 2024
R-4.5-linux-x86_64NOTENov 27 2024

Exports:distMat.KernelKnndistMat.knn.index.distKernelKnnKernelKnnCVknn.index.dist

Dependencies:RcppRcppArmadillo

binary classification using the ionosphere data

Rendered frombinary_classification_using_the_ionosphere_data.Rmdusingknitr::rmarkdownon Nov 27 2024.

Last update: 2017-10-30
Started: 2016-07-09

Image classification of the MNIST and CIFAR-10 data using KernelKnn and HOG (histogram of oriented gradients)

Rendered fromimage_classification_using_MNIST_CIFAR_data.Rmdusingknitr::rmarkdownon Nov 27 2024.

Last update: 2016-09-08
Started: 2016-07-09

Regression using the Housing data

Rendered fromregression_using_the_housing_data.Rmdusingknitr::rmarkdownon Nov 27 2024.

Last update: 2017-10-30
Started: 2016-07-09