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robregcc: Robust Regression with Compositional Covariates

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <doi:10.48550/arXiv.1909.04990>.

Version: 1.1
Depends: R (≥ 3.5.0), stats, utils
Imports: Rcpp (≥ 0.12.0), MASS, magrittr, graphics
LinkingTo: Rcpp, RcppArmadillo
Published: 2020-07-25
DOI: 10.32614/CRAN.package.robregcc
Author: Aditya Mishra [aut, cre], Christian Muller [ctb]
Maintainer: Aditya Mishra <amishra at flatironinstitute.org>
License: GPL (≥ 3.0)
URL: https://arxiv.org/abs/1909.04990, https://github.com/amishra-stats/robregcc
NeedsCompilation: yes
CRAN checks: robregcc results

Documentation:

Reference manual: robregcc.pdf

Downloads:

Package source: robregcc_1.1.tar.gz
Windows binaries: r-devel: robregcc_1.1.zip, r-release: robregcc_1.1.zip, r-oldrel: robregcc_1.1.zip
macOS binaries: r-release (arm64): robregcc_1.1.tgz, r-oldrel (arm64): robregcc_1.1.tgz, r-release (x86_64): robregcc_1.1.tgz, r-oldrel (x86_64): robregcc_1.1.tgz
Old sources: robregcc archive

Linking:

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These binaries (installable software) and packages are in development.
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