fitHeavyTail | Mean and Covariance Matrix Estimation under Heavy Tails

 by   convexfi R Version: v0.2.0 License: GPL-3.0

kandi X-RAY | fitHeavyTail Summary

kandi X-RAY | fitHeavyTail Summary

fitHeavyTail is a R library. fitHeavyTail has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution.
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              fitHeavyTail has a low active ecosystem.
              It has 16 star(s) with 3 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of fitHeavyTail is v0.2.0

            kandi-Quality Quality

              fitHeavyTail has no bugs reported.

            kandi-Security Security

              fitHeavyTail has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              fitHeavyTail is licensed under the GPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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              fitHeavyTail releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            fitHeavyTail Examples and Code Snippets

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            Install fitHeavyTail

            The package can be installed from CRAN or GitHub:.
            To illustrate the simple usage of the package fitHeavyTail, let's start by generating some multivariate data under a Student's $t$ distribution with significant heavy tails (degrees of freedom $\nu=4$):.

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