Linear discriminant analysis (LDA) is a powerful tool in building classifiers with easy computation and interpretation. Recent advancements in science technology have led to the popularity of datasets with high dimensions, high orders and complicated structure. Such datasetes motivate the generalization of LDA in various research directions. The R package TULIP integrates several popular high-dimensional LDA-based methods and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification. Functions are included for model fitting, cross validation and prediction. In addition, motivated by datasets with diverse sources of predictors, we further include functions for covariate adjustment. Our package is carefully tailored for low storage and high computation efficiency. Moreover, our package is the first R package for many of these methods, providing great convenience to researchers in this area.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-025.zip
TULIP, msda, sparseLDA, MASS, Matrix, tensr, glmnet
Econometrics, Multivariate, NumericalMathematics, Distributions, Environmetrics, MachineLearning, Psychometrics, Robust, SocialSciences, Survival, TeachingStatistics
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For attribution, please cite this work as
Pan, et al., "The R Journal: TULIP: A Toolbox for Linear Discriminant Analysis with Penalties", The R Journal, 2021
BibTeX citation
@article{RJ-2021-025,
author = {Pan, Yuqing and Mai, Qing and Zhang, Xin},
title = {The R Journal: TULIP: A Toolbox for Linear Discriminant Analysis with Penalties},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-025},
doi = {10.32614/RJ-2021-025},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {61-81}
}