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.

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

• High-Dimensional Regularized Discriminant Analysis (hdrda) from Ramey et al. (2014)

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

• Diagonal Linear Discriminant Analysis from Dudoit et al. (2002) (dlda)
• Diagonal Quadratic Discriminant Analysis from Dudoit et al. (2002) (dqda)
• Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse (lda_pseudo)
• Linear Discriminant Analysis (LDA) with the Schafer-Strimmer estimator (lda_schafer)
• Linear Discriminant Analysis (LDA) with the Thomaz-Kitani-Gillies estimator (lda_thomaz)
• Minimum Distance Empirical Bayesian Estimator from Srivistava and Kubokawa (2007) (mdeb)
• Minimum Distance Rule using Modified Empirical Bayes from Srivistava and Kubokawa (2007) (mdmeb)
• Minimum Distance Rule using Moore-Penrose Inverse from Srivistava and Kubokawa (2007) (mdmp)
• Shrinkage-based Diagonal Linear Discriminant Analysis from Pang et al. (2009) (sdlda)
• Shrinkage-based Diagonal Quadratic Discriminant Analysis from Pang et al. (2009) (sdqda)
• Shrinkage-mean-based Diagonal Linear Discriminant Analysis from Tong et al. (2012) (smdlda)
• Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis from Tong et al. (2012) (smdqda)

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('sparsediscrim', 'ramhiser')