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
dlda                    Diagonal Linear Discriminant Analysis (DLDA)
dmvnorm_diag            Computes multivariate normal density with a
                        diagonal covariance matrix
dqda                    Diagonal Quadratic Discriminant Analysis (DQDA)
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).
hdrda                   High-Dimensional Regularized Discriminant
                        Analysis (HDRDA)
hdrda_cv                Helper function to optimize the HDRDA
                        classifier via cross-validation
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_thomaz              Linear Discriminant Analysis using the
                        Thomaz-Kitani-Gillies Covariance Matrix
                        Estimator
log_determinant         Computes the log determinant of a matrix.
mdeb                    The Minimum Distance Empirical Bayesian
                        Estimator (MDEB) classifier
mdmeb                   The Minimum Distance Rule using Modified
                        Empirical Bayes (MDMEB) classifier
mdmp                    The Minimum Distance Rule using Moore-Penrose
                        Inverse (MDMP) classifier
no_intercept            Removes the intercept term from a formula if it
                        is included
plot.hdrda_cv           Plots a heatmap of cross-validation error grid
                        for a HDRDA classifier object.
posterior_probs         Computes posterior probabilities via Bayes
                        Theorem under normality
print.dlda              Outputs the summary for a DLDA classifier
                        object.
print.dqda              Outputs the summary for a DQDA classifier
                        object.
print.hdrda             Outputs the summary for a HDRDA classifier
                        object.
print.lda_pseudo        Outputs the summary for a lda_pseudo classifier
                        object.
print.lda_schafer       Outputs the summary for a lda_schafer
                        classifier object.
print.lda_thomaz        Outputs the summary for a lda_thomaz classifier
                        object.
print.mdeb              Outputs the summary for a MDEB classifier
                        object.
print.mdmeb             Outputs the summary for a MDMEB classifier
                        object.
print.mdmp              Outputs the summary for a MDMP classifier
                        object.
print.sdlda             Outputs the summary for a SDLDA classifier
                        object.
print.sdqda             Outputs the summary for a SDQDA classifier
                        object.
print.smdlda            Outputs the summary for a SmDLDA classifier
                        object.
print.smdqda            Outputs the summary for a SmDQDA classifier
                        object.
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_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).
sdlda                   Shrinkage-based Diagonal Linear Discriminant
                        Analysis (SDLDA)
sdqda                   Shrinkage-based Diagonal Quadratic Discriminant
                        Analysis (SDQDA)
smdlda                  Shrinkage-mean-based Diagonal Linear
                        Discriminant Analysis (SmDLDA) from Tong, Chen,
                        and Zhao (2012)
smdqda                  Shrinkage-mean-based Diagonal Quadratic
                        Discriminant Analysis (SmDQDA) from Tong, Chen,
                        and Zhao (2012)
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
update_hdrda            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).
