Man pages for TULIP
A Toolbox for Linear Discriminant Analysis with Penalties
| 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... |
| csa | Colorimetric sensor array (CSA) data |
| cvcatch | 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... |
| 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... |
| 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 |
