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:

```
library(devtools)
install_github('ramhiser/sparsediscrim')
```

The `sparsediscrim`

package features the following classifier (the R function is included within parentheses):

- High-Dimensional Regularized Discriminant Analysis (
`hdrda`

) from Ramey et al. (2015)

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`

)