README.md

sparsediscrim

Build Status

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):

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) | dlda | | Diagonal Quadratic Discriminant Analysis | Dudoit et al. (2002) | dqda | | Shrinkage-based Diagonal Linear Discriminant Analysis | Pang et al. (2009) | sdlda | | Shrinkage-based Diagonal Quadratic Discriminant Analysis | Pang et al. (2009) | sdqda | | Shrinkage-mean-based Diagonal Linear Discriminant Analysis | Tong et al. (2012) | smdlda | | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis | Tong et al. (2012) | smdqda | | Minimum Distance Empirical Bayesian Estimator (MDEB) | Srivistava and Kubokawa (2007) | mdeb | | Minimum Distance Rule using Modified Empirical Bayes (MDMEB) | Srivistava and Kubokawa (2007) | mdmeb | | Minimum Distance Rule using Moore-Penrose Inverse (MDMP) | Srivistava and Kubokawa (2007) | mdmp |

We also include modifications to Linear Discriminant Analysis (LDA) with regularized covariance-matrix estimators:



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sparsediscrim documentation built on Aug. 14, 2017, 5:10 p.m.