A fast reimplementation of several density-based algorithms of
the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial
clustering of applications with noise) and OPTICS (ordering points to identify
the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier
factor) algorithm. The implementations use the kd-tree data structure (from
library ANN) for faster k-nearest neighbor search. An R interface to fast kNN
and fixed-radius NN search is also provided.
See Hahsler M, Piekenbrock M and Doran D (2019) <doi:10.18637/jss.v091.i01>.
Rcpp (>= 1.0.0), graphics, stats, methods

A fast reimplementation of several density-based algorithms of
the DBSCAN family for spatial data. Includes the clustering algorithms
DBSCAN (density-based spatial clustering of applications with noise)
and HDBSCAN (hierarchical DBSCAN), the ordering algorithm
OPTICS (ordering points to identify the clustering structure),
and the outlier detection algorithm LOF (local outlier factor).
The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search.
An R interface to fast kNN and fixed-radius NN search is also provided.
Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
fpc, microbenchmark, testthat, dendextend, igraph, knitr,
rmarkdown, DMwR

Boj, Eva <evaboj@ub.edu>, Caballe, Adria
<adria.caballe@upc.edu>, Delicado, Pedro
<pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>.
This package contains functions for distance-based
prediction methods. These are methods for prediction where
predictor information is coded as a matrix of distances between
individuals. Distances can either be directly input as an
interdistances matrix, a squared interdistances matrix, an
inner-products matrix or computed from observed explanatory
variables.

This package contains functions for distance-based
prediction methods.  These are methods for prediction where
predictor information is coded as a matrix of distances between
individuals.  Distances can either be directly input as an
interdistances matrix, a squared interdistances matrix, an
inner-products matrix or computed from observed explanatory
variables.