cov_mle

data matrix with <code>n</code> observations and <code>p</code> feature vectors

logical value. If TRUE, assumes the population covariance matrix
is diagonal. By default, we assume that <code>diag</code> is <code>FALSE</code>.

diag

For a sample matrix, <code>x</code>, we compute the sample covariance matrix of the
data as the maximum likelihood estimator (MLE) of the population covariance
matrix.

A collection of sparse and regularized discriminant analysis
methods intended for small-sample, high-dimensional data sets. The package
features the High-Dimensional Regularized Discriminant Analysis classifier
from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
<doi:10.1093/bioinformatics/btr690>.

Max Kuhn

cov_mle: Computes the maximum likelihood estimator for the sample covariance matrix under the assumption of multivariate normality.

Description

Usage

Arguments

Value

Details