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modelsummary creates beautiful and customizable tables to summarize statistical models in R.

Results from several models are presented side-by-side. Tables can be echoed to the R console or viewed in the RStudio Viewer. They can be saved to HTML, PDF, Text/Markdown, LaTeX, MS Word, RTF, JPG, and PNG formats. Tables can easily be embedded in dynamic document pipelines like Rmarkdown, knitr, or Sweave.

These two tables were created using modelsummary without any manual editing at all. The first is an HTML table. The second is a LaTeX table.


Why should I use modelsummary?

Here are a few benefits of modelsummary over some alternative packages:


modelsummary is very easy to use. This simple call often suffices:


mod <- lm(y ~ x, dat)

The command above will automatically display a summary table in the Rstudio Viewer or in a web browser. All you need is one word to change the output format. For example, a text-only version of the table can be printed to the Console by typing:

msummary(mod, "markdown")

Tables in Microsoft Word and LaTeX formats can be saved to file by typing:

msummary(mod, "table.docx")
msummary(mod, "table.tex")


Information: The package offers many intuitive and powerful utilities to customize the information reported in a summary table. 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.

Appearance: Thanks to the gt, kableExtra, huxtable, and flextable packages, the appearance of modelsummary tables is endlessly customizable. The appearance customization page shows tables with colored cells, weird text, spanning column labels, row groups, titles, source notes, footnotes, significance stars, and more. This only scratches the surface of possibilities.

Supported models: Thanks to the broom package, modelsummary supports dozens of statistical models out-of-the-box. Installing other packages can extend the capabilities further (e.g., broom.mixed). It is also very easy to add or customize your own models.

Output formats: modelsummary tables can be saved to HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, RTF, JPG, or PNG formats. They can also be inserted seamlessly in Rmarkdown documents to produce automated documents and reports in PDF, HTML, RTF, or Microsoft Word formats.


modelsummary is dangerous! It allows users to do stupid stuff like replacing their intercepts by squirrels.


modelsummary is reliably dangerous! The package is developed using a suite of unit tests, so it (probably) won’t break.


modelsummary does not try to do everything. Instead, it leverages the incredible work of the R community. By building on top of the broom package, modelsummary already supports dozens of model types out-of-the-box. modelsummary also supports four of the most popular table-building and customization packages: gt, kableExtra, huxtable, and flextable. packages. By using those packages, modelsummary allows users to produce beautiful, endlessly customizable tables in a wide variety of formats, including HTML, PDF, LaTeX, Markdown, and MS Word.

One benefit of this community-focused approach is that when external packages improve, modelsummary improves as well. Another benefit is that leveraging external packages allows modelsummary to have a massively simplified codebase (relative to other similar packages). This should improve long term code maintainability, and allow contributors to participate through GitHub.


You can install modelsummary from CRAN:


If you want the very latest version, install it from Github:


Getting started

We begin by loading the modelsummary package and by downloading data from the RDatasets repository:


url <- ''
dat <- read.csv(url) 

We estimate a linear model and call the msummary function to display the results:

mod <- lm(Donations ~ Crime_prop, data = dat)

To summarize multiple models side-by-side, we store them in a list. If the items in that list are named, the names will be used as column labels:

models <- list()
models[['OLS 1']] <- lm(Donations ~ Literacy + Clergy, data = dat)
models[['Poisson 1']] <- glm(Donations ~ Literacy + Commerce, family = poisson, data = dat)
models[['OLS 2']] <- lm(Crime_pers ~ Literacy + Clergy, data = dat)
models[['Poisson 2']] <- glm(Crime_pers ~ Literacy + Commerce, family = poisson, data = dat)
models[['OLS 3']] <- lm(Crime_prop ~ Literacy + Clergy, data = dat)


In Rstudio, the image below will be displayed automatically in the “Viewer” window. When running R from a terminal or from the basic R interface, this table should appear in your browser.

The same table can be printed in text-only format to the R Console:

msummary(models, 'markdown')

|            |OLS 1      |Poisson 1   |OLS 2      |Poisson 2   |OLS 3      |
|(Intercept) |7948.667   |8.241       |16259.384  |9.876       |11243.544  |
|            |(2078.276) |(0.006)     |(2611.140) |(0.003)     |(1011.240) |
|Clergy      |15.257     |            |77.148     |            |-16.376    |
|            |(25.735)   |            |(32.334)   |            |(12.522)   |
|Literacy    |-39.121    |0.003       |3.680      |-0.000      |-68.507    |
|            |(37.052)   |(0.000)     |(46.552)   |(0.000)     |(18.029)   |
|Commerce    |           |0.011       |           |0.001       |           |
|            |           |(0.000)     |           |(0.000)     |           |
|Num.Obs.    |86         |86          |86         |86          |86         |
|R2          |0.020      |            |0.065      |            |0.152      |
|Adj.R2      |-0.003     |            |0.043      |            |0.132      |
|AIC         |1740.8     |274160.8    |1780.0     |257564.4    |1616.9     |
|BIC         |1750.6     |274168.2    |1789.9     |257571.7    |1626.7     |
|Log.Lik.    |-866.392   |-137077.401 |-886.021   |-128779.186 |-804.441   |

Saving and viewing: output formats

There are four ways to display and save modelsummary tables.

  1. Display in the R Console, the RStudio Viewer, or a web browser.
  2. Save a table to file.
  3. Insert a table in Rmarkdown or knitr documents,
  4. Convert the table to human-readable html, latex, or markdown code.

modelsummary uses sensible defaults to choose an appopriate table-making package for each output format (gt, kableExtra, flextable, or huxtable). This table summarizes how to modify modelsummary’s output argument to display and save tables:

In the above table, checkmarks identify the default table-making package used for each output format. Dots identify supported alternatives. To use those alternatives, we set global options such as:

options(modelsummary_html = 'kableExtra')
options(modelsummary_latex = 'gt')
options(modelsummary_word = 'huxtable')
options(modelsummary_png = 'gt')

Alternative packages

There are several excellent alternatives to draw model summary tables in R: