gpg, rmarkdown, magrittr, shiny, miniUI, rstudioapi (>=
0.11), DT

gpg, rmarkdown, magrittr, shiny, miniUI, rstudioapi (>=
0.11), DT, digest

An Accurate kNN Implementation with Multiple Distance Measures
c(
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>.
Rcpp (>= 1.0.2), RcppEigen (>= 0.3.3.5.0), Matrix, methods
Philipp Angerer [cre, aut] (<https://orcid.org/0000-0002-0369-2888>),
David Crane [cph, aut]

Finds the p number of near neighbours for every point in a given dataset in O(M log M) time.

Boxian Wei & Fan Yang & Xinmiao Wang & Yanni Ge
Multi-distance based k-Nearest Neighbors Classification
with K Threshold Value Check and Same K_i Problem Dealing,
Missing Observations Filling

Muti-distance based k-Nearest Neighbors Classification
with K Threshold Value Check and Same K_i Problem Dealing,
Missing Observations Filling

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.

k-nn classification with variable selection inside leaves of a tree
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
one such k-nn classifier in each leaf.

Nearest-neighbor classification with categorical variables
This program scales categorical variables in such a way as to
make NN classification as accurate as possible. It also handles continuous
variables and prior probabilities, and does intelligent variable selection
and estimation of error rates and the right number of NN's.