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bigdatadist  

Distances for Machine Learning and Statistics in the Context of Big Data
View on CRAN: Click here


Download and install bigdatadist package within the R console
Install from CRAN:
install.packages("bigdatadist")

Install from Github:
library("remotes")
install_github("cran/bigdatadist")

Install by package version:
library("remotes")
install_version("bigdatadist", "1.1")



Attach the package and use:
library("bigdatadist")
Maintained by
Gabriel Martos
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-07-02
Latest Update: 2018-09-24
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) ; Martos et al (2018) ; Hernandez et al (2018, submitted); Martos et al (2018, submitted).
How to cite:
Gabriel Martos (2018). bigdatadist: Distances for Machine Learning and Statistics in the Context of Big Data. R package version 1.1, https://cran.r-project.org/web/packages/bigdatadist. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0 (2018-07-02 10:50)
Other packages that cited bigdatadist R package
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Other R packages that bigdatadist depends, imports, suggests or enhances
Complete documentation for bigdatadist
Functions, R codes and Examples using the bigdatadist R package
Some associated functions: Ausmale . entropy.fd . entropy . fdframe . gmdepth.fd . gmdepth . kmdepth.fd . levelsetdist . merval . rkhs . 
Some associated R codes: entropy.R . entropy.fd.R . fdframe.R . gmdepth.R . gmdepth.fd.R . hidden.R . kmdepth.fd.R . levelsetdist.R . rkhs.R . workhorse.R .  Full bigdatadist package functions and examples
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