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