factor) algorithm. The implementations use the kd-tree data structure (from
Imports: Rcpp (>= 0.12.12), graphics, stats, methods

See Hahsler M, Piekenbrock M and Doran D (2019) <doi:10.18637/jss.v091.i01>.
Imports: Rcpp (>= 1.0.0), graphics, stats, methods

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.

Author: Boj, Eva <evaboj@ub.edu>, Caballe, Adria
<pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>.
Description: 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

prediction methods.  These are methods for prediction where
individuals.  Distances can either be directly input as an

Author: Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>.
Description: 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.

Description: Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.