R: A Toolbox for Linear Discriminant Analysis with Penalties

# A Toolbox for Linear Discriminant Analysis with Penalties

## 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).