codalm: Transformation-Free Linear Regression for Compositional Outcomes and Predictors

Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2020) <arXiv:2004.07881> for transformation-free linear regression for compositional outcomes and predictors.

Version: 0.1.0
Imports: SQUAREM (≥ 2020.3), future, future.apply
Suggests: knitr, ggtern, gtools, remotes, testthat, markdown
Published: 2020-06-25
Author: Jacob Fiksel ORCID iD [aut, cre], Abhirup Datta [ctb]
Maintainer: Jacob Fiksel <jfiksel at gmail.com>
BugReports: https://github.com/jfiksel/codalm/issues
License: GPL-2
URL: https://github.com/jfiksel/codalm
NeedsCompilation: no
Materials: README NEWS
CRAN checks: codalm results

Downloads:

Reference manual: codalm.pdf
Vignettes: How to use codalm
Package source: codalm_0.1.0.tar.gz
Windows binaries: r-devel: codalm_0.1.0.zip, r-release: codalm_0.1.0.zip, r-oldrel: codalm_0.1.0.zip
macOS binaries: r-release: codalm_0.1.0.tgz, r-oldrel: codalm_0.1.0.tgz

Reverse dependencies:

Reverse imports: Compositional

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