Transformation-Free Linear Regression for Compositional Outcomes and Predictors

Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) for transformation-free linear regression for compositional outcomes and predictors.


The codalm R package

The codalm R package implements the methods as described in A Transformation-free Linear Regression for Compositional Outcomes and Predictors (Fiksel et al., 2022,https://doi.org/10.1111/biom.13465)

Installation

codalm is now available on CRAN and can be installed using

install.packages('codalm')

You can also install the development version of codalm for github using the remotes package.

remotes::install_github("jfiksel/codalm")

Example

View the vignette (https://jfiksel.github.io/codalm/articles/codalm_quickstart.html) for examples of how to use the codalm package

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("codalm")

0.1.3 by Sandipan Pramanik, 10 months ago


https://github.com/jfiksel/codalm


Report a bug at https://github.com/jfiksel/codalm/issues


Browse source code at https://github.com/cran/codalm


Authors: Jacob Fiksel [aut] , Abhirup Datta [ctb] , Sandipan Pramanik [cre, ctb] (ORCID:


Documentation:   PDF Manual  


GPL-2 license


Imports SQUAREM, future, future.apply

Suggests knitr, gtools, remotes, testthat, rmarkdown, ggtern, ggplot2


Suggested by Compositional, OrdinalCompositions.


See at CRAN