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HDclassif  

High Dimensional Supervised Classification and Clustering
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("HDclassif", "2.2.2")



Attach the package and use:
library("HDclassif")
Maintained by
Laurent Berge
[Scholar Profile | Author Map]
First Published: 2010-07-26
Latest Update: 2023-08-23
Description:
Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.
How to cite:
Laurent Berge (2010). HDclassif: High Dimensional Supervised Classification and Clustering. R package version 2.2.2, https://cran.r-project.org/web/packages/HDclassif. Accessed 29 Apr. 2025.
Previous versions and publish date:
1.0 (2010-07-26 23:02), 1.1.1 (2010-12-20 15:50), 1.1.2 (2011-02-10 09:22), 1.1.3 (2011-04-12 18:01), 1.1 (2010-11-16 11:01), 1.2.1 (2012-01-08 13:03), 1.2.2 (2013-01-08 15:58), 1.2.3 (2015-02-16 06:48), 1.2 (2011-08-04 13:28), 1.3 (2015-07-01 15:05), 2.0.2 (2016-12-20 15:48), 2.0 (2016-08-01 17:29), 2.1.0 (2018-05-11 18:34), 2.2.0 (2019-11-19 19:20), 2.2.1 (2023-08-23 09:50)
Other packages that cited HDclassif R package
View HDclassif citation profile
Other R packages that HDclassif depends, imports, suggests or enhances
Complete documentation for HDclassif
Functions, R codes and Examples using the HDclassif R package
Some associated functions: Crabs . HDclassif-package . demo_hddc . hdda . hddc . hdmda . plot.hdc . predict.hdc . predict.hdmda . setHDclassif.show . simuldata . slopeHeuristic . wine . 
Some associated R codes: controls.R . demo.R . hdc.R . hdda.R . hddc.R . mdaHD.R . onLoad.R .  Full HDclassif package functions and examples
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