R: Sparse and Regularized Discriminant Analysis

# Sparse and Regularized Discriminant Analysis

## Documentation for package ‘sparsediscrim’ version 0.3.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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_diag.default | Diagonal Linear Discriminant Analysis (DLDA)
lda_diag.formula | Diagonal Linear Discriminant Analysis (DLDA)
lda_eigen | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier
lda_eigen.default | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier
lda_eigen.formula | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier
lda_emp_bayes | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier
lda_emp_bayes.default | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier
lda_emp_bayes.formula | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier
lda_emp_bayes_eigen | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier
lda_emp_bayes_eigen.default | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier
lda_emp_bayes_eigen.formula | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier
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_shrink_cov | Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA)
lda_shrink_cov.default | Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA)
lda_shrink_cov.formula | 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_shrink_mean.default | Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012)
lda_shrink_mean.formula | 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
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.
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
predict.lda_diag | Diagonal Linear Discriminant Analysis (DLDA)
predict.lda_eigen | The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier
predict.lda_emp_bayes | The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier
predict.lda_emp_bayes_eigen | The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier
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_shrink_cov | Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA)
predict.lda_shrink_mean | Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012)
predict.lda_thomaz | Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance Matrix Estimator
predict.qda_diag | Diagonal Quadratic Discriminant Analysis (DQDA)
predict.qda_shrink_cov | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA)
predict.qda_shrink_mean | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012)
predict.rda_high_dim | High-Dimensional Regularized Discriminant Analysis (HDRDA)
qda_diag | Diagonal Quadratic Discriminant Analysis (DQDA)
qda_diag.default | Diagonal Quadratic Discriminant Analysis (DQDA)
qda_diag.formula | Diagonal Quadratic Discriminant Analysis (DQDA)
qda_shrink_cov | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA)
qda_shrink_cov.default | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA)
qda_shrink_cov.formula | Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA)
qda_shrink_mean | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012)
qda_shrink_mean.default | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012)
qda_shrink_mean.formula | 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.default | High-Dimensional Regularized Discriminant Analysis (HDRDA)
rda_high_dim.formula | 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).