Mean and Covariance Matrix Estimation under Heavy Tails

Robust estimation methods for the mean vector and covariance matrix from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t. Additionally, a factor model structure can be specified for the covariance matrix. The package is based on the papers: Sun, Babu, and Palomar (2014), Sun, Babu, and Palomar (2015), Liu and Rubin (1995), and Zhou, Liu, Kumar, and Palomar (2019).


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.2 by Daniel P. Palomar, a year ago

Report a bug at

Browse source code at

Authors: Daniel P. Palomar [cre, aut] , Rui Zhou [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports ICSNP, mvtnorm, stats

Suggests knitr, ggplot2, prettydoc, reshape2, rmarkdown, R.rsp, testthat

See at CRAN