The R Journal: article published in 2020, volume 12:2

TULIP: A Toolbox for Linear Discriminant Analysis with Penalties PDF download
Yuqing Pan, Qing Mai and Xin Zhang , The R Journal (2020) 12:2, pages 61-81.

Abstract 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.

Received: 2020-05-06; online 2021-01-20, supplementary material, (4.4 Kb)
CRAN packages: TULIP, msda, sparseLDA, MASS, Matrix, tensr, glmnet
CRAN Task Views implied by cited CRAN packages: Econometrics, Multivariate, NumericalMathematics, Distributions, Environmetrics, MachineLearning, Psychometrics, Robust, SocialSciences, Survival, TeachingStatistics


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This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-025,
  author = {Yuqing Pan and Qing Mai and Xin Zhang},
  title = {{TULIP: A Toolbox for Linear Discriminant Analysis with
          Penalties}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-025},
  url = {https://doi.org/10.32614/RJ-2021-025},
  pages = {61--81},
  volume = {12},
  number = {2}
}