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| 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) |
| dlda.default | Diagonal Linear Discriminant Analysis (DLDA) |
| dlda.formula | Diagonal Linear Discriminant Analysis (DLDA) |
| dmvnorm_diag | Computes multivariate normal density with a diagonal covariance matrix |
| dqda | Diagonal Quadratic Discriminant Analysis (DQDA) |
| dqda.default | Diagonal Quadratic Discriminant Analysis (DQDA) |
| dqda.formula | 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.default | High-Dimensional Regularized Discriminant Analysis (HDRDA) |
| hdrda.formula | 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_pseudo.default | Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse |
| lda_pseudo.formula | Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse |
| lda_schafer | Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix Estimator |
| lda_schafer.default | Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix Estimator |
| lda_schafer.formula | Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix Estimator |
| lda_thomaz | Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance Matrix Estimator |
| lda_thomaz.default | Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance Matrix Estimator |
| lda_thomaz.formula | 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 |
| mdeb.default | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier |
| mdeb.formula | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier |
| mdmeb | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier |
| mdmeb.default | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier |
| mdmeb.formula | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier |
| mdmp | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier |
| mdmp.default | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier |
| mdmp.formula | 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 |
| predict.dlda | Diagonal Linear Discriminant Analysis (DLDA) |
| predict.dqda | Diagonal Quadratic Discriminant Analysis (DQDA) |
| predict.hdrda | High-Dimensional Regularized Discriminant Analysis (HDRDA) |
| predict.lda_pseudo | Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse |
| predict.lda_schafer | Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix Estimator |
| predict.lda_thomaz | Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance Matrix Estimator |
| predict.mdeb | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier |
| predict.mdmeb | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier |
| predict.mdmp | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier |
| predict.sdlda | Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA) |
| predict.sdqda | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA) |
| predict.smdlda | Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012) |
| predict.smdqda | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012) |
| 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) |
| sdlda.default | Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA) |
| sdlda.formula | Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA) |
| sdqda | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA) |
| sdqda.default | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA) |
| sdqda.formula | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA) |
| smdlda | Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012) |
| smdlda.default | Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012) |
| smdlda.formula | 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) |
| smdqda.default | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012) |
| smdqda.formula | 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). |