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Sparse and Regularized Discriminant Analysis in R
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DESCRIPTION
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NAMESPACE
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README.md

README.md

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 (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)
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