Model Summary Tables


Create beautiful and customizable tables to summarize several statistical models side-by-side. This function supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, Typst, PDF, PowerPoint, Excel, RTF, JPG, or PNG. The appearance of the tables can be customized extensively by specifying the output argument, and by using functions from one of the supported table customization packages: tinytable, kableExtra, gt, flextable, huxtable, DT. For more information, see the Details and Examples sections below, and the vignettes on the modelsummary website:


  output = "default",
  fmt = 3,
  estimate = "estimate",
  statistic = "std.error",
  vcov = NULL,
  conf_level = 0.95,
  exponentiate = FALSE,
  stars = FALSE,
  shape = term + statistic ~ model,
  coef_map = NULL,
  coef_omit = NULL,
  coef_rename = FALSE,
  gof_map = NULL,
  gof_omit = NULL,
  gof_function = NULL,
  group_map = NULL,
  add_columns = NULL,
  add_rows = NULL,
  align = NULL,
  notes = NULL,
  title = NULL,
  escape = TRUE,



a model, (named) list of models, or nested list of models.

  • Single model: modelsummary(model)

  • Unnamed list of models: modelsummary(list(model1, model2))

    • Models are labelled automatically. The default label style can be altered by setting a global option. See below.

  • Named list of models: modelsummary(list(“A”=model1, “B”=model2))

    • Models are labelled using the list names.

  • Nested list of models:

    • When using the shape argument with "rbind", "rcollapse", or "cbind" values, models can be a nested list of models to display "panels" or "stacks" of regression models. See the shape argument documentation and examples below.


filename or object type (character string)

  • Supported filename extensions: .docx, .html, .tex, .md, .txt, .csv, .xlsx, .png, .jpg

  • Supported object types: "default", "html", "markdown", "latex", "latex_tabular", "typst", "data.frame", "tinytable", "gt", "kableExtra", "huxtable", "flextable", "DT", "jupyter". The "modelsummary_list" value produces a lightweight object which can be saved and fed back to the modelsummary function.

  • The "default" output format can be set to "tinytable", "kableExtra", "gt", "flextable", "huxtable", "DT", or "markdown"

    • If the user does not choose a default value, the packages listed above are tried in sequence.

    • Session-specific configuration: options(“modelsummary_factory_default” = “gt”)

    • Persistent configuration: config_modelsummary(output = “markdown”)

  • Warning: Users should not supply a file name to the output argument if they intend to customize the table with external packages. See the ‘Details’ section.

  • LaTeX compilation requires the booktabs and siunitx packages, but siunitx can be disabled or replaced with global options. See the ‘Details’ section.


how to format numeric values: integer, user-supplied function, or modelsummary function.

  • Integer: Number of decimal digits

  • User-supplied functions:

    • Any function which accepts a numeric vector and returns a character vector of the same length.

  • modelsummary functions:

    • fmt = fmt_significant(2): Two significant digits (at the term-level)

    • fmt = fmt_decimal(digits = 2, pdigits = 3): Decimal digits for estimate and p values

    • fmt = fmt_sprintf(“%.3f”): See ?sprintf

    • fmt = fmt_term(“(Intercept)” = 1, “X” = 2): Format terms differently

    • fmt = fmt_statistic(“estimate” = 1, “r.squared” = 6): Format statistics differently.

    • fmt = fmt_identity(): unformatted raw values

  • string:

  • Note on LaTeX output: To ensure proper typography, all numeric entries are enclosed in the command, which requires the siunitx package to be loaded in the LaTeX preamble. This behavior can be altered with global options. See the ‘Details’ section.


a single string or a character vector of length equal to the number of models. Valid entries include any column name of the data.frame produced by get_estimates(model), and strings with curly braces compatible with the glue package format. Examples:

  • “estimate”

  • “{estimate} ({std.error}){stars}”

  • “{estimate} [{conf.low}, {conf.high}]”


vector of strings or glue strings which select uncertainty statistics to report vertically below the estimate. NULL omits all uncertainty statistics.

  • "", "std.error", "statistic", "p.value", "conf.low", "conf.high", or any column name produced by get_estimates(model)

  • glue package strings with braces, with or without R functions, such as:

    • “{p.value} [{conf.low}, {conf.high}]”

    • “Std.Error: {std.error}”

    • “{exp(estimate) * std.error}”

    • Numbers are automatically rounded and converted to strings. To apply functions to their numeric values, as in the last glue example, users must set fmt=NULL.

    • Parentheses are added automatically unless the string includes glue curly braces {}.

  • Notes:

    • The names of the statistic are used a column names when using the shape argument to display statistics as columns:

      • statistic=c(“p”=“p.value”, “[”=”conf.low”, ”]”=“conf.high”)

    • Some statistics are not supported for all models. See column names in get_estimates(model), and visit the website to learn how to add custom statistics.


robust standard errors and other manual statistics. The vcov argument accepts six types of input (see the ‘Details’ and ‘Examples’ sections below):

  • NULL returns the default uncertainty estimates of the model object

  • string, vector, or (named) list of strings. "iid", "classical", and "constant" are aliases for NULL, which returns the model’s default uncertainty estimates. The strings "HC", "HC0", "HC1" (alias: "stata"), "HC2", "HC3" (alias: "robust"), "HC4", "HC4m", "HC5", "HAC", "NeweyWest", "Andrews", "panel-corrected", "outer-product", and "weave" use variance-covariance matrices computed using functions from the sandwich package, or equivalent method. "BS", "bootstrap", "residual", "mammen", "webb", "xy", "wild" use the sandwich::vcovBS(). The behavior of those functions can (and sometimes must) be altered by passing arguments to sandwich directly from modelsummary through the ellipsis (), but it is safer to define your own custom functions as described in the next bullet.

  • function or (named) list of functions which return variance-covariance matrices with row and column names equal to the names of your coefficient estimates (e.g., stats::vcov, sandwich::vcovHC, function(x) vcovPC(x, cluster=“country”)).

  • formula or (named) list of formulas with the cluster variable(s) on the right-hand side (e.g., ~clusterid).

  • named list of length(models) variance-covariance matrices with row and column names equal to the names of your coefficient estimates.

  • a named list of length(models) vectors with names equal to the names of your coefficient estimates. See ‘Examples’ section below. Warning: since this list of vectors can include arbitrary strings or numbers, modelsummary cannot automatically calculate p values. The stars argument may thus use incorrect significance thresholds when vcov is a list of vectors.

conf_level numeric value between 0 and 1. confidence level to use for confidence intervals. Setting this argument to NULL does not extract confidence intervals, which can be faster for some models.
exponentiate TRUE, FALSE, or logical vector of length equal to the number of models. If TRUE, the estimate, conf.low, and conf.high statistics are exponentiated, and the std.error is transformed to exp(estimate)*std.error.

to indicate statistical significance

  • FALSE (default): no significance stars.

  • TRUE: +=.1, =.05, =.01, =0.001

  • Named numeric vector for custom stars such as c(’*’ = .1, ‘+’ = .05)

  • Note: a legend will not be inserted at the bottom of the table when the estimate or statistic arguments use "glue strings" with stars.


NULL, formula, or string which determines the shape of a table.

  • NULL: Default shape with terms in rows and models in columns.

  • Formula: The left side determines what appears on rows, and the right side determines what appears on columns. The formula can include one or more group identifier(s) to display related terms together, which can be useful for models with multivariate outcomes or grouped coefficients (See examples section below). The group identifier(s) must be column names produced by: get_estimates(model). The group identifier(s) can be combined with the term identifier in a single column by using the colon to represent an interaction. If an incomplete formula is supplied (e.g., ~statistic), modelsummary tries to complete it automatically. Goodness-of-fit statistics are only appended to the bottom of the table when model is on the right hand side of the formula (i.e., columns). Potential shape values include:

    • term + statistic ~ model: default

    • term ~ model + statistic: statistics in separate columns

    • model + statistic ~ term: models in rows and terms in columns

    • term + response + statistic ~ model: term and group id in separate columns

    • term : response + statistic ~ model: term and group id in a single column

    • term ~ response

  • String: "cbind", "rbind", "rcollapse"

    • "cbind": side-by-side models with autmoatic spanning column headers to group models (tinytable only feature).

    • "rbind" or "rcollapse": "panels" or "stacks" of regression models.

    • the models argument must be a (potentially named) nested list of models.

    • Unnamed nested list with 2 panels: list(list(model1, model2), list(model3, model4))

    • Named nested list with 2 panels: list(“Panel A” = list(model1, model2), “Panel B” = list(model3, model4))

    • Named panels and named models: list(“Panel A” = list(“(I)” = model1, “(II)” = model2), “Panel B” = list(“(I)” = model3, “(II)” = model4))

    • "rbind": Bind the rows of independent regression tables

    • "rcollapse": Bind the rows of regression tables and create a panel at the bottom where we "collapse" goodness-of-fit statistics which are identical across models.

coef_map character vector. Subset, rename, and reorder coefficients. Coefficients omitted from this vector are omitted from the table. The order of the vector determines the order of the table. coef_map can be a named or an unnamed character vector. If coef_map is a named vector, its values define the labels that must appear in the table, and its names identify the original term names stored in the model object: c(“hp:mpg”=“HPxM/G”). See Examples section below.

integer vector or regular expression to identify which coefficients to omit (or keep) from the table. Positive integers determine which coefficients to omit. Negative integers determine which coefficients to keep. A regular expression can be used to omit coefficients, and perl-compatible "negative lookaheads" can be used to specify which coefficients to keep in the table. Examples:

  • c(2, 3, 5): omits the second, third, and fifth coefficients.

  • c(-2, -3, -5): negative values keep the second, third, and fifth coefficients.

  • “ei”: omit coefficients matching the "ei" substring.

  • “^Volume$”: omit the "Volume" coefficient.

  • “ei|rc”: omit coefficients matching either the "ei" or the "rc" substrings.

  • “^(?!Vol)”: keep coefficients starting with "Vol" (inverse match using a negative lookahead).

  • “^(?!.*ei)“: keep coefficients matching the”ei" substring.

  • “^(?!.ei|.pt)”: keep coefficients matching either the "ei" or the "pt" substrings.

  • See the Examples section below for complete code.


logical, named or unnamed character vector, or function

  • Logical: TRUE renames variables based on the "label" attribute of each column. See the Example section below.

  • Unnamed character vector of length equal to the number of coefficients in the final table, after coef_omit is applied.

  • Named character vector: Values refer to the variable names that will appear in the table. Names refer to the original term names stored in the model object. Ex: c("hp:mpg"="hp X mpg")

  • Function: Accepts a character vector of the model’s term names and returns a named vector like the one described above. The modelsummary package supplies a coef_rename() function which can do common cleaning tasks: modelsummary(model, coef_rename = coef_rename)


rename, reorder, and omit goodness-of-fit statistics and other model information. This argument accepts 4 types of values:

  • NULL (default): the modelsummary::gof_map dictionary is used for formatting, and all unknown statistic are included.

  • character vector: "all", "none", or a vector of statistics such as c(“rmse”, “nobs”, “r.squared”). Elements correspond to colnames in the data.frame produced by get_gof(model). The modelsummary::gof_map default dictionary is used to format and rename statistics.

  • NA: excludes all statistics from the bottom part of the table.

  • data.frame with 3 columns named "raw", "clean", "fmt". Unknown statistics are omitted. See the ‘Examples’ section below. The fmt column in this data frame only accepts integers. For more flexibility, use a list of lists, as described in the next bullet.

  • list of lists, each of which includes 3 elements named "raw", "clean", "fmt". Unknown statistics are omitted. See the ‘Examples section below’.


string regular expression (perl-compatible) used to determine which statistics to omit from the bottom section of the table. A "negative lookahead" can be used to specify which statistics to keep in the table. Examples:

  • “IC”: omit statistics matching the "IC" substring.

  • “BIC|AIC”: omit statistics matching the "AIC" or "BIC" substrings.

  • “^(?!.*IC)“: keep statistics matching the”IC" substring.

gof_function function which accepts a model object in the model argument and returns a 1-row data.frame with one custom goodness-of-fit statistic per column.
group_map named or unnamed character vector. Subset, rename, and reorder coefficient groups specified a grouping variable specified in the shape argument formula. This argument behaves like coef_map.
add_columns a data.frame (or tibble) with the same number of rows as #’ your main table. By default, rows are appended to the bottom of the table. You can define a "position" attribute of integers to set the columns positions. See Examples section below.
add_rows a data.frame (or tibble) with the same number of columns as your main table. By default, rows are appended to the bottom of the table. You can define a "position" attribute of integers to set the row positions. See Examples section below.

A string with a number of characters equal to the number of columns in the table (e.g., align = “lcc”). Valid characters: l, c, r, d.

  • "l": left-aligned column

  • "c": centered column

  • "r": right-aligned column

  • "d": dot-aligned column. For LaTeX/PDF output, this option requires at least version 3.0.25 of the siunitx LaTeX package. See the LaTeX preamble help section below for commands to insert in your LaTeX preamble.

notes list or vector of notes to append to the bottom of the table.
title string. Cross-reference labels should be added with Quarto or Rmarkdown chunk options when applicable. When saving standalone LaTeX files, users can add a label such as \label{tab:mytable} directly to the title string, while also specifying escape=FALSE.
escape boolean TRUE escapes or substitutes LaTeX/HTML characters which could prevent the file from compiling/displaying. TRUE escapes all cells, captions, and notes. Users can have more fine-grained control by setting escape=FALSE and using an external command such as: modelsummary(model, “latex”) |> tinytable::format_tt(tab, j=1:5, escape=TRUE)

all other arguments are passed through to three functions. See the documentation of these functions for lists of available arguments.

  • parameters::model_parameters extracts parameter estimates. Available arguments depend on model type, but include:

    • standardize, include_reference, centrality, dispersion, test, ci_method, prior, diagnostic, rope_range, power, cluster, etc.

  • performance::model_performance extracts goodness-of-fit statistics. Available arguments depend on model type, but include:

    • metrics, estimator, etc.

  • tinytable::tt, kableExtra::kbl or gt::gt draw tables, depending on the value of the output argument.



The modelsummary_list output is a lightweight format which can be used to save model results, so they can be fed back to modelsummary later to avoid extracting results again.

When a file name with a valid extension is supplied to the output argument, the table is written immediately to file. If you want to customize your table by post-processing it with an external package, you need to choose a different output format and saving mechanism. Unfortunately, the approach differs from package to package:

  • tinytable: set output=“tinytable”, post-process your table, and use the tinytable::save_tt function.

  • gt: set output=“gt”, post-process your table, and use the gt::gtsave function.

  • kableExtra: set output to your destination format (e.g., "latex", "html", "markdown"), post-process your table, and use kableExtra::save_kable function.


To use a string such as "robust" or "HC0", your model must be supported by the sandwich package. This includes objects such as: lm, glm, survreg, coxph, mlogit, polr, hurdle, zeroinfl, and more.

NULL, "classical", "iid", and "constant" are aliases which do not modify uncertainty estimates and simply report the default standard errors stored in the model object.

One-sided formulas such as ~clusterid are passed to the sandwich::vcovCL function.

Matrices and functions producing variance-covariance matrices are first passed to lmtest. If this does not work, modelsummary attempts to take the square root of the diagonal to adjust "std.error", but the other uncertainty estimates are not be adjusted.

Numeric vectors are formatted according to fmt and placed in brackets. Character vectors printed as given, without parentheses.

If your model type is supported by the lmtest package, the vcov argument will try to use that package to adjust all the uncertainty estimates, including "std.error", "statistic", "p.value", and "". If your model is not supported by lmtest, only the "std.error" will be adjusted by, for example, taking the square root of the matrix’s diagonal.


a regression table in a format determined by the output argument.

Global Options

The behavior of modelsummary can be modified by setting global options. For example:

  • options(modelsummary_model_labels = “roman”)

The rest of this section describes each of the options above.

Model labels: default column names

These global option changes the style of the default column headers:

  • options(modelsummary_model_labels = “roman”)

  • options(modelsummary_panel_labels = “roman”)

The supported styles are: "model", "panel", "arabic", "letters", "roman", "(arabic)", "(letters)", "(roman)"

The panel-specific option is only used when shape=“rbind”

Table-making packages

modelsummary supports 6 table-making packages: tinytable, kableExtra, gt, flextable, huxtable, and DT. Some of these packages have overlapping functionalities. To change the default backend used for a specific file format, you can use ’ the options function:

options(modelsummary_factory_html = ‘kableExtra’) options(modelsummary_factory_word = ‘huxtable’) options(modelsummary_factory_png = ‘gt’) options(modelsummary_factory_latex = ‘gt’) options(modelsummary_factory_latex_tabular = ‘kableExtra’)

Table themes

Change the look of tables in an automated and replicable way, using the modelsummary theming functionality. See the vignette:

  • modelsummary_theme_gt

  • modelsummary_theme_kableExtra

  • modelsummary_theme_huxtable

  • modelsummary_theme_flextable

  • modelsummary_theme_dataframe

Model extraction functions

modelsummary can use two sets of packages to extract information from statistical models: the easystats family (performance and parameters) and broom. By default, it uses easystats first and then falls back on broom in case of failure. You can change the order of priorities or include goodness-of-fit extracted by both packages by setting:

options(modelsummary_get = “easystats”)

options(modelsummary_get = “broom”)

options(modelsummary_get = “all”)

Formatting numeric entries

By default, LaTeX tables enclose all numeric entries in the command from the siunitx package. To prevent this behavior, or to enclose numbers in dollar signs (for LaTeX math mode), users can call:

options(modelsummary_format_numeric_latex = “plain”)

options(modelsummary_format_numeric_latex = “mathmode”)

A similar option can be used to display numerical entries using MathJax in HTML tables:

options(modelsummary_format_numeric_html = “mathjax”)

LaTeX preamble

When creating LaTeX via the tinytable backend (default in version 2.0.0 and later), it is useful to include the following commands in the LaTeX preamble of your documents. Note that they are added automatically when compiling Rmarkdown or Quarto documents (except when the modelsummary() calls are cached).


Parallel computation

It can take a long time to compute and extract summary statistics from certain models (e.g., Bayesian). In those cases, users can parallelize the process. Since parallelization occurs at the model level, no speedup is available for tables with a single model. Users on mac or linux can launch parallel computation using the built-in parallel package. All they need to do is supply a mc.cores argument which will be pushed forward to the parallel::mclapply function:

modelsummary(model_list, mc.cores = 5)

All users can also use the future.apply package to parallelize model summaries. For example, to use 4 cores to extract results:

plan(multicore, workers = 4)
options("modelsummary_future" = TRUE)

Note that the "multicore" plan only parallelizes under mac or linux. Windows users can use plan(multisession) instead. However, note that the first time modelsummary() is called under multisession can be a fair bit longer, because of extra costs in passing data to and loading required packages on to workers. Subsequent calls to modelsummary() will often be much faster.

Some users have reported difficult to reproduce errors when using the future package with some packages. The future parallelization in modelsummary can be disabled by calling:

options(“modelsummary_future” = FALSE)


Arel-Bundock V (2022). “modelsummary: Data and Model Summaries in R.” Journal of Statistical Software, 103(1), 1-23. .’



# The `modelsummary` website includes \emph{many} examples and tutorials:


# load data and estimate models
models <- list()
models[['Bivariate']] <- lm(Girth ~ Height, data = trees)
models[['Multivariate']] <- lm(Girth ~ Height + Volume, data = trees)

# simple table

# statistic
modelsummary(models, statistic = NULL)

modelsummary(models, statistic = 'p.value')

modelsummary(models, statistic = 'statistic')

modelsummary(models, statistic = '', conf_level = 0.99)

modelsummary(models, statistic = c("t = {statistic}",
                                   "se = {std.error}",

# estimate
  statistic = NULL,
  estimate = "{estimate} [{conf.low}, {conf.high}]")

  estimate = c("{estimate}{stars}",
               "{estimate} ({std.error})"))

# vcov
modelsummary(models, vcov = "robust")

modelsummary(models, vcov = list("classical", "stata"))

modelsummary(models, vcov = sandwich::vcovHC)

  vcov = list(stats::vcov, sandwich::vcovHC))

  vcov = list(c("(Intercept)"="", "Height"="!"),
              c("(Intercept)"="", "Height"="!", "Volume"="!!")))

# vcov with custom names
  vcov = list("Stata Corp" = "stata",
              "Newey Lewis & the News" = "NeweyWest"))

# fmt
mod <- lm(mpg ~ hp + drat + qsec, data = mtcars)

modelsummary(mod, fmt = 3)

modelsummary(mod, fmt = fmt_significant(3))

modelsummary(mod, fmt = NULL)

modelsummary(mod, fmt = fmt_decimal(4))

modelsummary(mod, fmt = fmt_sprintf("%.5f"))

modelsummary(mod, fmt = fmt_statistic(estimate = 4, = 1), statistic = "")

modelsummary(mod, fmt = fmt_term(hp = 4, drat = 1, default = 2))

m <- lm(mpg ~ I(hp * 1000) + drat, data = mtcars)
f <- function(x) format(x, digits = 3, nsmall = 2, scientific = FALSE, trim = TRUE)
modelsummary(m, fmt = f, gof_map = NA)

# coef_rename
modelsummary(models, coef_rename = c('Volume' = 'Large', 'Height' = 'Tall'))

modelsummary(models, coef_rename = toupper)

modelsummary(models, coef_rename = coef_rename)

# coef_rename = TRUE for variable labels
datlab <- mtcars
datlab$cyl <- factor(datlab$cyl)
attr(datlab$hp, "label") <- "Horsepower"
attr(datlab$cyl, "label") <- "Cylinders"
modlab <- lm(mpg ~ hp * drat + cyl, data = datlab)
modelsummary(modlab, coef_rename = TRUE)

# coef_rename: unnamed vector of length equal to the number of terms in the final table
m <- lm(hp ~ mpg + factor(cyl), data = mtcars)
modelsummary(m, coef_omit = -(3:4), coef_rename = c("Cyl 6", "Cyl 8"))

# coef_map
modelsummary(models, coef_map = c('Volume' = 'Large', 'Height' = 'Tall'))

modelsummary(models, coef_map = c('Volume', 'Height'))

# coef_omit: omit the first and second coefficients
modelsummary(models, coef_omit = 1:2)

# coef_omit: omit coefficients matching one substring
modelsummary(models, coef_omit = "ei", gof_omit = ".*")

# coef_omit: omit a specific coefficient
modelsummary(models, coef_omit = "^Volume$", gof_omit = ".*")

# coef_omit: omit coefficients matching either one of two substring
#modelsummary(models, coef_omit = "ei|rc", gof_omit = ".*")

# coef_omit: keep coefficients starting with a substring (using a negative lookahead)
#modelsummary(models, coef_omit = "^(?!Vol)", gof_omit = ".*")

# coef_omit: keep coefficients matching a substring
modelsummary(models, coef_omit = "^(?!.*ei|.*pt)", gof_omit = ".*")

# shape: multinomial model
multi <- multinom(factor(cyl) ~ mpg + hp, data = mtcars, trace = FALSE) 

# shape: term names and group ids in rows, models in columns
modelsummary(multi, shape = response ~ model)

# shape: term names and group ids in rows in a single column
modelsummary(multi, shape = term : response ~ model)

# shape: term names in rows and group ids in columns
modelsummary(multi, shape = term ~ response:model)

# shape = "rcollapse"
panels <- list(
    "Panel A: MPG" = list(
        "A" = lm(mpg ~ hp, data = mtcars),
        "B" = lm(mpg ~ hp + factor(gear), data = mtcars)),
    "Panel B: Displacement" = list(
        "A" = lm(disp ~ hp, data = mtcars),
        "C" = lm(disp ~ hp + factor(gear), data = mtcars))

# shape = "cbind"
modelsummary(panels, shape = "cbind")

    shape = "rbind",
    gof_map = c("nobs", "r.squared"))

# title
modelsummary(models, title = 'This is the title')

# title with LaTeX label (for numbering and referencing)
modelsummary(models, title = 'This is the title \\label{tab:description}', escape = FALSE)

# add_rows
rows <- tibble::tribble(~term, ~Bivariate, ~Multivariate,
  'Empty row', '-', '-',
  'Another empty row', '?', '?')
attr(rows, 'position') <- c(1, 3)
modelsummary(models, add_rows = rows)

# notes
modelsummary(models, notes = list('A first note', 'A second note'))

# gof_map: tribble
gm <- tribble(
  ~raw,        ~clean,      ~fmt,
  "r.squared", "R Squared", 5)
modelsummary(models, gof_map = gm)

# gof_map: list of lists
f <- function(x) format(round(x, 3), big.mark=",")
gm <- list(
  list("raw" = "nobs", "clean" = "N", "fmt" = f),
  list("raw" = "AIC", "clean" = "aic", "fmt" = f))
modelsummary(models, gof_map = gm)