Sparse and Regularized Discriminant Analysis

Documentation for package ‘sparsediscrim’ version 0.2

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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
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_cv	Helper function to optimize the HDRDA classifier via cross-validation
no_intercept	Removes the intercept term from a formula if it is included
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).
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).