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`

)

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')
```