fitHeavyTail: 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).

Version: 0.1.2
Imports: ICSNP, mvtnorm, stats
Suggests: knitr, ggplot2, prettydoc, reshape2, rmarkdown, R.rsp, testthat
Published: 2020-01-07
Author: Daniel P. Palomar [cre, aut], Rui Zhou [aut]
Maintainer: Daniel P. Palomar <daniel.p.palomar at>
License: GPL-3
NeedsCompilation: no
Citation: fitHeavyTail citation info
Materials: README NEWS
CRAN checks: fitHeavyTail results


Reference manual: fitHeavyTail.pdf
Vignettes: Mean Vector and Covariance Matrix Estimation under Heavy Tails
Package source: fitHeavyTail_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): fitHeavyTail_0.1.2.tgz, r-release (x86_64): fitHeavyTail_0.1.2.tgz, r-oldrel: fitHeavyTail_0.1.2.tgz
Old sources: fitHeavyTail archive


Please use the canonical form to link to this page.