Sparse and Regularized Discriminant Analysis in R


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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.


You can install the stable version on CRAN:

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

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



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:

  • Moore-Penrose Pseudo-Inverse (lda_pseudo)
  • Schafer-Strimmer estimator (lda_schafer)
  • Thomaz-Kitani-Gillies estimator (lda_thomaz)