A Toolbox for Linear Discriminant Analysis with Penalties
Adjust tensor for covariates.
Adjust vector for covariates.
Fit a CATCH model and predict categorical response.
Fit a CATCH model for matrix and predict categorical response.
Cross validation for direct sparse discriminant analysis
Cross-validation for DSDA/MSDA through function msda
Cross validation for semiparametric sparse discriminant analysis
Cross-validation for CATCH
Direct sparse discriminant analysis
Solution path for direct sparse discriminant analysis
Direct sparse discriminant analysis
Fits a regularization path of Sparse Discriminant Analysis and predict...
Predict categorical responses for matrix/tensor data.
Prediction for direct sparse discriminant analysis
Predict categorical responses for vector data.
Prediction for semiparametric sparse discriminant analysis
Solution path for regularized optimal affine discriminant
Solution path for semiparametric sparse discriminant analysis
Simulate data
Simulate data
Solution path for sparse discriminant analysis
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) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.