A Toolbox for Linear Discriminant Analysis with Penalties

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Documentation for package ‘TULIP’ version 1.0.2

• DESCRIPTION file.

Help Pages

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).