Package: bigdatadist
Version: 1.1
Date: 2018-09-23
Title: Distances for Machine Learning and Statistics in the Context of
        Big Data
Description: Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).
Authors@R: c(person("Gabriel", "Martos", role = c("aut", "cre"),
	     email = "gmartos@utdt.edu"),
	     person("Nicolas", "Hernandez", role = "aut",
	     email = "nicolas.hernandez@uc3m.es"))
Author: Gabriel Martos [aut, cre],
  Nicolas Hernandez [aut]
Depends: R (>= 3.4.0)
Maintainer: Gabriel Martos <gmartos@utdt.edu>
License: GPL (>= 3)
Repository: CRAN
Imports: MASS, FNN, rrcov, pdist
NeedsCompilation: yes
Packaged: 2018-09-24 13:14:03 UTC; gmartos
Date/Publication: 2018-09-24 13:30:03 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-27 15:00:43 UTC; unix
Archs: bigdatadist.so.dSYM
