https://github.com/ramhiser/sparsediscrim
Sparse and Regularized Discriminant Analysis in R
https://github.com/ramhiser/sparsediscrim
classifier high-dimensional-data machine-learning r
Last synced: 11 months ago
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Sparse and Regularized Discriminant Analysis in R
- Host: GitHub
- URL: https://github.com/ramhiser/sparsediscrim
- Owner: ramhiser
- License: other
- Created: 2010-10-14T23:38:21.000Z (over 15 years ago)
- Default Branch: master
- Last Pushed: 2020-11-15T20:08:12.000Z (over 5 years ago)
- Last Synced: 2025-06-01T07:21:05.484Z (12 months ago)
- Topics: classifier, high-dimensional-data, machine-learning, r
- Language: R
- Homepage:
- Size: 595 KB
- Stars: 14
- Watchers: 3
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# sparsediscrim
[](https://travis-ci.org/ramhiser/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](https://cran.r-project.org/package=sparsediscrim):
```r
install.packages('sparsediscrim', dependencies = TRUE)
```
If you prefer to download the latest version, instead type:
```r
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](https://arxiv.org/abs/1602.01182) (`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)](http://www.tandfonline.com/doi/abs/10.1198/016214502753479248) | `dlda` |
| Diagonal Quadratic Discriminant Analysis | [Dudoit et al. (2002)](http://www.tandfonline.com/doi/abs/10.1198/016214502753479248) | `dqda` |
| Shrinkage-based Diagonal Linear Discriminant Analysis | [Pang et al. (2009)](http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2009.01200.x/abstract) | `sdlda` |
| Shrinkage-based Diagonal Quadratic Discriminant Analysis | [Pang et al. (2009)](http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2009.01200.x/abstract) | `sdqda` |
| Shrinkage-mean-based Diagonal Linear Discriminant Analysis | [Tong et al. (2012)](http://bioinformatics.oxfordjournals.org/content/28/4/531.long) | `smdlda` |
| Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis | [Tong et al. (2012)](http://bioinformatics.oxfordjournals.org/content/28/4/531.long) | `smdqda` |
| Minimum Distance Empirical Bayesian Estimator (MDEB) | [Srivistava and Kubokawa (2007)](http://www.utstat.utoronto.ca/~srivasta/exp1.pdf) | `mdeb` |
| Minimum Distance Rule using Modified Empirical Bayes (MDMEB) | [Srivistava and Kubokawa (2007)](http://www.utstat.utoronto.ca/~srivasta/exp1.pdf) | `mdmeb` |
| Minimum Distance Rule using Moore-Penrose Inverse (MDMP) | [Srivistava and Kubokawa (2007)](http://www.utstat.utoronto.ca/~srivasta/exp1.pdf) | `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`)