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sparsediscrim

The R package sparsediscrim provides a collection of sparse and regularized discriminant analysis classifiers that are especially useful for when applied to small-sample, high-dimensional data sets.

Installation

You can install the stable version on CRAN:

install.packages('sparsediscrim', dependencies = TRUE)

If you prefer to download the latest version, instead type:

library(devtools)
install_github('ramhiser/sparsediscrim')

Classifiers

The sparsediscrim package features the following classifier (the R function is included within parentheses):

High-Dimensional Regularized Discriminant Analysis (rda_high_dim) from Ramey et al. (2015)

The sparsediscrim package also includes a variety of additional classifiers intended for small-sample, high-dimensional data sets. These include:

Classifier	Author	R Function
Diagonal Linear Discriminant Analysis	Dudoit et al. (2002)	`lda_diag`
Diagonal Quadratic Discriminant Analysis	Dudoit et al. (2002)	`qda_diag`
Shrinkage-based Diagonal Linear Discriminant Analysis	Pang et al. (2009)	`lda_shrink_cov`
Shrinkage-based Diagonal Quadratic Discriminant Analysis	Pang et al. (2009)	`qda_shrink_cov`
Shrinkage-mean-based Diagonal Linear Discriminant Analysis	Tong et al. (2012)	`lda_shrink_mean`
Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis	Tong et al. (2012)	`qda_shrink_mean`
Minimum Distance Empirical Bayesian Estimator (MDEB)	Srivistava and Kubokawa (2007)	`lda_emp_bayes`
Minimum Distance Rule using Modified Empirical Bayes (MDMEB)	Srivistava and Kubokawa (2007)	`lda_emp_bayes_eigen`
Minimum Distance Rule using Moore-Penrose Inverse (MDMP)	Srivistava and Kubokawa (2007)	`lda_eigen`