HiDimDA: High Dimensional Discriminant Analysis , Performs Linear Discriminant Analysis in High Dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection. (Source: http://cran.r-project.org/web/packages)
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Francesco Denti: intRinsic: an R package for model-based estimation of the intrinsic dimension of a dataset (2021) arXiv
- Yu, Weichang; Ormerod, John T.; Stewart, Michael: Variational discriminant analysis with variable selection (2020)
- Ledoit, Olivier; Wolf, Michael: Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions (2015)
- Silva, A. Pedro Duarte: Two-group classification with high-dimensional correlated data: a factor model approach (2011)