Supported models: Thanks to the broom and parameters, modelsummary supports hundreds of statistical models out-of-the-box. This only scratches the surface of possibilities. The appearance customization page shows tables with colored cells, weird text, spanning column labels, row groups, titles, source notes, footnotes, significance stars, and more. You can rename, reorder, subset or omit parameter estimates choose the set of goodness-of-fit statistics to include display various “robust” standard errors or confidence intervals add titles, footnotes, or source notes insert stars or custom characters to indicate levels of statistical significance or add rows with supplemental information about your models.Īppearance: Thanks to the gt, kableExtra, huxtable, flextable, and DT packages, the appearance of modelsummary tables is endlessly customizable. Information: The package offers many intuitive and powerful utilities to customize the information reported in a summary table. Modelsummary(mod, output = "table.docx") modelsummary(mod, output = "table.tex") Flexible Here are a few benefits of modelsummary over some alternative packages: Easy With these functions, you can create tables and plots like these: datasummary_df: Turn dataframes into nice tables with titles, notes, etc.datasummary_skim: Quick overview (“skim”) of a dataset.datasummary_correlation: Correlation tables.datasummary_balance: Balance tables with subgroup statistics and difference in means (aka “Table 1”).datasummary_crosstab: Cross-tabulations.datasummary: Powerful tool to create (multi-level) cross-tabs and data summaries.modelsummary: Regression tables with side-by-side models.Modelsummary includes two families of functions: The modelsummary package is designed to be simple, robust, modular, and extensible (Arel-Bundock, 2022). Tables and plots can be embedded seamlessly in rmarkdown, knitr, or Sweave dynamic documents. Tables can be exported to many output formats, including HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, Excel, RTF, PDF, and image files. The appearance of the tables produced by modelsummary can be customized using external packages such as kableExtra, gt, flextable, or huxtable the plots can be customized using ggplot2. Beyond model summaries, the package also includes a suite of tools to produce highly flexible data summary tables, such as dataset overviews, correlation matrices, (multi-level) cross-tabulations, and balance tables (also known as “Table 1”). It makes it easy to execute common tasks such as computing robust standard errors, adding significance stars, and manipulating coefficient and model labels. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. Modelsummary is a package to summarize data and statistical models in R. Modelsummary creates tables and plots to present descriptive statistics and to summarize statistical models in R.
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