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TULIP (version 1.0.2)

A Toolbox for Linear Discriminant Analysis with Penalties

Description

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) . Functions are included for covariate adjustment, model fitting, cross validation and prediction.

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Version

Install

install.packages('TULIP')

Monthly Downloads

155

Version

1.0.2

License

GPL-2

Maintainer

Yuqing Pan

Last Published

January 4th, 2021

Functions in TULIP (1.0.2)

SeSDA

Solution path for semiparametric sparse discriminant analysis
cv.SeSDA

Cross validation for semiparametric sparse discriminant analysis
catch

Fit a CATCH model and predict categorical response.
csa

Colorimetric sensor array (CSA) data
SOS

Solution path for sparse discriminant analysis
adjvec

Adjust vector for covariates.
adjten

Adjust tensor for covariates.
catch_matrix

Fit a CATCH model for matrix and predict categorical response.
ROAD

Solution path for regularized optimal affine discriminant
GDS1615

GDS1615 data introduced in Burczynski et al. (2012).
cv.catch

Cross-validation for CATCH
predict.catch

Predict categorical responses for matrix/tensor data.
predict.dsda

Prediction for direct sparse discriminant analysis
sim.tensor.cov

Simulate data
getnorm

Direct sparse discriminant analysis
dsda.all

Direct sparse discriminant analysis
dsda

Solution path for direct sparse discriminant analysis
predict.SeSDA

Prediction for semiparametric sparse discriminant analysis
sim.bi.vector

Simulate data
predict.msda

Predict categorical responses for vector data.
msda

Fits a regularization path of Sparse Discriminant Analysis and predicts
cv.dsda

Cross validation for direct sparse discriminant analysis
cv.msda

Cross-validation for DSDA/MSDA through function msda