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) 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

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)