A Toolbox for Linear Discriminant Analysis with Penalties

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Documentation for package ‘TULIP’ version 1.0.2

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adjten Adjust tensor for covariates.
adjvec Adjust vector for covariates.
catch Fit a CATCH model and predict categorical response.
catch_matrix Fit a CATCH model for matrix and predict categorical response.
csa Colorimetric sensor array (CSA) data
cv.catch Cross-validation for CATCH
cv.dsda Cross validation for direct sparse discriminant analysis
cv.msda Cross-validation for DSDA/MSDA through function 'msda'
cv.SeSDA Cross validation for semiparametric sparse discriminant analysis
dsda Solution path for direct sparse discriminant analysis
dsda.all Direct sparse discriminant analysis
GDS1615 GDS1615 data introduced in Burczynski et al. (2012).
getnorm Direct sparse discriminant analysis
msda Fits a regularization path of Sparse Discriminant Analysis and predicts
predict.catch Predict categorical responses for matrix/tensor data.
predict.dsda Prediction for direct sparse discriminant analysis
predict.msda Predict categorical responses for vector data.
predict.SeSDA Prediction for semiparametric sparse discriminant analysis
ROAD Solution path for regularized optimal affine discriminant
SeSDA Solution path for semiparametric sparse discriminant analysis
sim.bi.vector Simulate data
sim.tensor.cov Simulate data
SOS Solution path for sparse discriminant analysis
x GDS1615 data introduced in Burczynski et al. (2012).
y GDS1615 data introduced in Burczynski et al. (2012).