  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.

Computation