| Centers the observations in a matrix by their respective class sample means | center_data |
| Generates a p \times p autocorrelated covariance matrix | cov_autocorrelation |
| Generates a p \times p block-diagonal covariance matrix with autocorrelated blocks. | cov_block_autocorrelation |
| Computes the eigenvalue decomposition of the maximum likelihood estimators (MLE) of the covariance matrices for the given data matrix | cov_eigen |
| Generates a p \times p intraclass covariance matrix | cov_intraclass |
| Computes the covariance-matrix maximum likelihood estimators for each class and returns a list. | cov_list |
| Computes the maximum likelihood estimator for the sample covariance matrix under the assumption of multivariate normality. | cov_mle |
| Computes the pooled maximum likelihood estimator (MLE) for the common covariance matrix | cov_pool |
| Computes a shrunken version of the maximum likelihood estimator for the sample covariance matrix under the assumption of multivariate normality. | cov_shrink_diag |
| Randomly partitions data for cross-validation. | cv_partition |
| Computes estimates and ancillary information for diagonal classifiers | diag_estimates |
| Computes multivariate normal density with a diagonal covariance matrix | dmvnorm_diag |
| Generates data from 'K' multivariate normal data populations, where each population (class) has a covariance matrix consisting of block-diagonal autocorrelation matrices. | generate_blockdiag |
| Generates data from 'K' multivariate normal data populations, where each population (class) has an intraclass covariance matrix. | generate_intraclass |
| Bias correction function from Pang et al. (2009). | h |
| Diagonal Linear Discriminant Analysis (DLDA) | lda_diag lda_diag.default lda_diag.formula predict.lda_diag |
| The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier | lda_eigen lda_eigen.default lda_eigen.formula predict.lda_eigen |
| The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier | lda_emp_bayes lda_emp_bayes.default lda_emp_bayes.formula predict.lda_emp_bayes |
| The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier | lda_emp_bayes_eigen lda_emp_bayes_eigen.default lda_emp_bayes_eigen.formula predict.lda_emp_bayes_eigen |
| Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse | lda_pseudo lda_pseudo.default lda_pseudo.formula predict.lda_pseudo |
| Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix Estimator | lda_schafer lda_schafer.default lda_schafer.formula predict.lda_schafer |
| Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA) | lda_shrink_cov lda_shrink_cov.default lda_shrink_cov.formula predict.lda_shrink_cov |
| Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012) | lda_shrink_mean lda_shrink_mean.default lda_shrink_mean.formula predict.lda_shrink_mean |
| Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance Matrix Estimator | lda_thomaz lda_thomaz.default lda_thomaz.formula predict.lda_thomaz |
| Computes the log determinant of a matrix. | log_determinant |
| Removes the intercept term from a formula if it is included | no_intercept |
| Plots a heatmap of cross-validation error grid for a HDRDA classifier object. | plot.rda_high_dim_cv |
| Computes posterior probabilities via Bayes Theorem under normality | posterior_probs |
| Diagonal Quadratic Discriminant Analysis (DQDA) | predict.qda_diag qda_diag qda_diag.default qda_diag.formula |
| Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA) | predict.qda_shrink_cov qda_shrink_cov qda_shrink_cov.default qda_shrink_cov.formula |
| Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012) | predict.qda_shrink_mean qda_shrink_mean qda_shrink_mean.default qda_shrink_mean.formula |
| Quadratic form of a matrix and a vector | quadform |
| Quadratic Form of the inverse of a matrix and a vector | quadform_inv |
| Calculates the RDA covariance-matrix estimators for each class | rda_cov |
| High-Dimensional Regularized Discriminant Analysis (HDRDA) | predict.rda_high_dim rda_high_dim rda_high_dim.default rda_high_dim.formula |
| Helper function to optimize the HDRDA classifier via cross-validation | rda_high_dim_cv |
| Computes the observation weights for each class for the HDRDA classifier | rda_weights |
| Computes estimates and ancillary information for regularized discriminant classifiers | regdiscrim_estimates |
| Stein Risk function from Pang et al. (2009). | risk_stein |
| Computes the inverse of a symmetric, positive-definite matrix using the Cholesky decomposition | solve_chol |
| Tong et al. (2012)'s Lindley-type Shrunken Mean Estimator | tong_mean_shrinkage |
| Example bivariate classification data from caret | two_class_sim_data |
| Helper function to update tuning parameters for the HDRDA classifier | update_rda_high_dim |
| Shrinkage-based estimator of variances for each feature from Pang et al. (2009). | var_shrinkage |
