Suggests: gpg, rmarkdown, magrittr, shiny, miniUI, rstudioapi (>=

Author: Ernest Liu, Daisy Sun, Aaron Swoboda.

Title: An Accurate kNN Implementation with Multiple Distance Measures
person('Philipp', 'Angerer', NULL, 'philipp.angerer@helmholtz-muenchen.de', c('cre', 'aut'), comment = c(ORCID = '0000-0002-0369-2888')),
person('David', 'Crane', NULL, 'dncrane@gmail.com', c('cph', 'aut')))
Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix.
The implementation is based on cover trees introduced by
Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.
Imports: Rcpp (>= 1.0.2), RcppEigen (>= 0.3.3.5.0), Matrix, methods
Author: Philipp Angerer [cre, aut] (<https://orcid.org/0000-0002-0369-2888>),

Description: Finds the p number of near neighbours for every point in a given dataset in O(M log M) time.
Packaged: Mon Feb 23 10:43:56 2009; hornik

Packaged: Tue Nov 15 08:28:39 2005; sekemp

Title: Multi-distance based k-Nearest Neighbors
Author: Boxian Wei & Fan Yang & Xinmiao Wang & Yanni Ge
Special Acknowledgement: Zhihong Huang & Weixian Jing
Description: Multi-distance based k-Nearest Neighbors Classification
with K Threshold Value Check and Same K_i Problem Dealing,
Packaged: 2012-07-13 11:56:28 UTC; BOXIANWEI

Title: Muti-distance based k-Nearest Neighbors
Description: Muti-distance based k-Nearest Neighbors Classification
Packaged: 2012-05-30 12:21:16 UTC; BOXIANWEI

Title: Independence tests and benchmarks
Description: This package provides the implementation of an exact formula of the
ith nearest neighbour distance distribution and implementations of tests of
independence based on that formula. Furthermore the package provides a
general framework to benchmark tests of independence.

Title: k-nn classification with variable selection inside leaves of a tree
Description: Construct or predict with k-nearest-neighbor classifiers,
using cross-validation to select k, choose variables (by forward
or backwards selection), and choose scaling (from among no scaling,
scaling each column by its SD, or scaling each column by its MAD).
The finished classifier will consist of a classification tree with