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 gmail.com>
BugReports: https://github.com/dppalomar/fitHeavyTail/issues
License: GPL-3
URL: https://github.com/dppalomar/fitHeavyTail
NeedsCompilation: no
Citation: fitHeavyTail citation info
Materials: README NEWS
CRAN checks: fitHeavyTail results

Downloads:

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: fitHeavyTail_0.1.2.zip, r-devel-gcc8: fitHeavyTail_0.1.2.zip, r-release: fitHeavyTail_0.1.2.zip, r-oldrel: fitHeavyTail_0.1.2.zip
OS X binaries: r-release: fitHeavyTail_0.1.2.tgz, r-oldrel: fitHeavyTail_0.1.2.tgz
Old sources: fitHeavyTail archive

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