Visualising and Interpreting Statistical Models Fit to Compositional Data R package DImodelsVis version 1.0.4 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. Software 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 Rishabh Vishwakarma <vishwakr@tcd.ie> Comprehensive R Archive Network (CRAN) Rishabh Vishwakarma [aut, cre] (ORCID: <https://orcid.org/0000-0002-4847-3494>), Caroline Brophy [aut], Laura Byrne [aut], Catherine Hurley [aut] GPL (>= 3) 2025-10-08 application/tgz https://CRAN.R-project.org/package=DImodelsVis doi:10.32614/CRAN.package.DImodelsVis en-US