DImodelsVis: Visualising and Interpreting Statistical Models Fit to Compositional Data

Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object.

Version: 1.0.4
Imports: cli, colorspace, DImodels (≥ 1.3.3), dplyr (≥ 1.0.0), forcats, ggfortify, ggplot2, ggtext (≥ 0.1.2), glue, grDevices, insight, methods, metR, PieGlyph, plotwidgets, rlang, scales, stats, tidyr, utils
Suggests: DImodelsMulti (≥ 1.0.0), knitr, rmarkdown, spelling, plotly, randomForest, cowplot, nnet, testthat (≥ 3.0.0), vdiffr
Published: 2025年10月08日
Author: Rishabh Vishwakarma ORCID iD [aut, cre], Caroline Brophy [aut], Laura Byrne [aut], Catherine Hurley [aut]
Maintainer: Rishabh Vishwakarma <vishwakr at tcd.ie>
License: GPL (≥ 3)
NeedsCompilation: no
Language: en-US
Materials: README, NEWS
CRAN checks: DImodelsVis results

Documentation:

Reference manual: DImodelsVis.html , DImodelsVis.pdf

Downloads:

Windows binaries: r-devel: DImodelsVis_1.0.4.zip, r-release: DImodelsVis_1.0.4.zip, r-oldrel: DImodelsVis_1.0.4.zip
macOS binaries: r-release (arm64): DImodelsVis_1.0.4.tgz, r-oldrel (arm64): DImodelsVis_1.0.4.tgz, r-release (x86_64): DImodelsVis_1.0.4.tgz, r-oldrel (x86_64): DImodelsVis_1.0.4.tgz

Linking:

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