### Install modelsummary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/get_started.html

Install the modelsummary package using the remotes package. This is the first step to using the package.

```r
remotes::install_github("vincentarelbundock/modelsummary")
```

--------------------------------

### Install modelsummary and dependencies from R-Universe

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/README.md

Install the development version of modelsummary and its dependencies from R-Universe. Ensure to restart R after installation.

```r
install.packages(
    c("modelsummary", "tinytable", "insight", "performance", "parameters"),
    repos = c(
        "https://vincentarelbundock.r-universe.dev",
        "https://easystats.r-universe.dev"))
```

--------------------------------

### Install modelsummary from CRAN

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/README.md

Install the modelsummary package from the Comprehensive R Archive Network (CRAN).

```r
install.packages('modelsummary')
```

--------------------------------

### Basic Model Summary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Generates a basic model summary. No specific setup or imports are required beyond having the 'modelsummary' package installed.

```r
library(modelsummary)
library(lme4)

# Load example data
data(sleepstudy)

# Fit a linear mixed-effects model
model <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)

# Generate a basic model summary
modelsummary(model)
```

--------------------------------

### Install Development Versions

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/NEWS.html

Use the update_modelsummary() function to easily install the development versions of modelsummary and its dependencies. This is primarily for developers or users reporting bugs.

```R
update_modelsummary()
```

--------------------------------

### Using Default gof_map as a Starting Point

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/modelsummary.html

Filter and select columns from the default `gof_map` to create a custom list for display. This is useful for starting with a predefined set of statistics and modifying it.

```R
gof_map <- modelsummary::gof_map %>%
  dplyr::filter(!omit) %>%
  dplyr::select(-omit) %>%
  plyr::dlply(1, c)
```

--------------------------------

### Set default table-making backend for HTML

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_correlation.html

Specify the default table-making package to use for generating HTML tables. 'kableExtra' is used in this example.

```R
options(modelsummary_factory_html = ‘kableExtra’)
```

--------------------------------

### Set default table-making backend for PNG

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_correlation.html

Specify the default table-making package to use for generating PNG images of tables. 'gt' is used in this example.

```R
options(modelsummary_factory_png = ‘gt’)
```

--------------------------------

### Set default table-making backend for Word

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_correlation.html

Specify the default table-making package to use for generating Word tables. 'huxtable' is used in this example.

```R
options(modelsummary_factory_word = ‘huxtable’)
```

--------------------------------

### Load Data and Estimate Models

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Loads data from a URL and estimates five different statistical models. This setup is used for demonstrating table customization.

```R
library(modelsummary)

url <- "https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv"
dat <- read.csv(url, na.strings = "")

models <- list(
    I = lm(Donations ~ Literacy, data = dat),
    II = lm(Crime_pers ~ Literacy, data = dat),
    III = lm(Crime_prop ~ Literacy + Clergy, data = dat),
    IV = glm(Crime_pers ~ Literacy + Clergy, family = poisson, data = dat),
    V = glm(Donations ~ Literacy + Clergy, family = poisson, data = dat)
)
```

--------------------------------

### Set default table-making backend for LaTeX

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_correlation.html

Specify the default table-making package to use for generating LaTeX tables. 'gt' is used in this example.

```R
options(modelsummary_factory_latex = ‘gt’)
```

--------------------------------

### Create a basic model summary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/get_started.html

Generate a standard statistical model summary using the modelsummary function. This example uses a simple linear model.

```r
fit <- lm(mpg ~ hp + wt, data = mtcars)
modelsummary(fit)
```

--------------------------------

### Generate a Model Summary with a Specific Theme and Font Size

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

This example demonstrates combining multiple appearance options, such as applying a 'markdown' theme and setting a 'normalsize' font.

```r
modelsummary(model, theme = 'markdown', options = list('font_size' = 'normalsize'))
```

--------------------------------

### Example of coef_omit with negative lookahead to keep coefficients

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/modelsummary.html

Shows how to use a negative lookahead in a regular expression with `coef_omit` to keep coefficients that do not start with 'Vol'.

```R
"^(?!Vol)"
```

--------------------------------

### Applying Predefined Themes

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Demonstrates the use of predefined themes to quickly change the overall appearance of the summary tables. This simplifies styling for consistent reports.

```r
# Use the 'toluene' theme
modelsummary(fit, theme = "toluene")

# Use the 'sky' theme
modelsummary(fit, theme = "sky")
```

--------------------------------

### Interactive Mode Notice for modelsummary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/LICENSE.html

This is an example of a short notice displayed when the modelsummary program starts in interactive mode, indicating copyright, warranty, and redistribution conditions.

```text
modelsummary Copyright (C) 2024 Vincent Arel-Bundock
This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type 'show c' for details.
```

--------------------------------

### Using Different Themes for Tables

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Provides examples of applying various built-in themes to change the visual style of the summary tables. This offers quick ways to alter the table's appearance.

```r
modelsummary(fit, theme = "basic")
modelsummary(fit, theme = "scientific")
modelsummary(fit, theme = "halway")
```

--------------------------------

### Using Themes for Table Styling

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Apply pre-defined themes to tables for quick styling using the `theme` argument. This allows for consistent and professional-looking tables.

```r
modelsummary(model, theme = "striped") # Apply a striped theme
```

```r
modelsummary(model, theme = "ிறார்") # Apply a LaTeX-style theme
```

--------------------------------

### Basic modelsummary Table in Quarto

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/modelsummary.html

Demonstrates a minimal Quarto document that generates a modelsummary table from an lm model. This example shows basic integration and cross-referencing capabilities.

```yaml
---
format: pdf
title: Example
---

@tbl-mtcars shows that cars with high horse power get low miles per gallon.


::: {#tbl-mtcars .cell tbl-cap='Horse Powers vs. Miles per Gallon'}

```{.r .cell-code}
library(modelsummary)
mod <- lm(mpg ~ hp, mtcars)
modelsummary(mod)
```

:::
```

--------------------------------

### Keep Coefficients Starting with 'Lit'

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/modelsummary.html

Uses a negative lookahead in `coef_omit` to keep coefficients that start with 'Lit'. Set `gof_map` to NA to omit goodness-of-fit statistics.

```r
modelsummary(models, coef_omit = "^(?!Lit)", gof_map = NA)
```

--------------------------------

### Basic sprintf Usage

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/fmt_sprintf.html

Demonstrates the basic usage of sprintf for string formatting. This is useful for creating formatted output strings.

```javascript
const r = setTimeout(() => {
    fn.apply(this, args);
    timer = throttle = false;
  }, ms);
};
};
```

--------------------------------

### Selecting Specific Columns

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Illustrates how to choose which columns to display in the summary table. This allows users to tailor the output to their specific analytical needs.

```r
modelsummary(fit,
  columns = c("term", "estimate", "p.value"), # Only show term, estimate, and p-value
  fmt = 3)
```

--------------------------------

### Tinytable Cell Styling Example

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_correlation_format.html

JavaScript code for styling specific cells in a Tinytable generated by modelsummary. This example demonstrates how to apply custom CSS classes to cells based on their position.

```javascript
var cellsToStyle = [
  { positions: [ { i: '3', j: 2 }, { i: '3', j: 3 }, { i: '3', j: 4 } ], css_id: 'tinytable_css_tppaxe9x9orotslra7g1'},
  { positions: [ { i: '1', j: 2 }, { i: '2', j: 2 }, { i: '1', j: 3 }, { i: '2', j: 3 }, { i: '1', j: 4 }, { i: '2', j: 4 } ], css_id: 'tinytable_css_tu7gor6a98enva64jib5'},
  { positions: [ { i: '0', j: 2 }, { i: '0', j: 3 }, { i: '0', j: 4 } ], css_id: 'tinytable_css_l9757xkokpspz36a146x'},
  { positions: [ { i: '3', j: 1 } ], css_id: 'tinytable_css_c9v5ondhns8adeld2olr'},
  { positions: [ { i: '1', j: 1 }, { i: '2', j: 1 } ], css_id: 'tinytable_css_i3j2tt5bm2ivtgdp25oi'},
  { positions: [ { i: '0', j: 1 } ], css_id: 'tinytable_css_q654vfevyhixwnkrb8o9'},
];

cellsToStyle.forEach(function (group) {
  group.positions.forEach(function (cell) {
    tableFns_hw37sz5ck53q20b3pnja.styleCell(cell.i, cell.j, group.css_id);
  });
});
```

--------------------------------

### Tinytable Styling Example

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_skim.html

This JavaScript code demonstrates how to create and style tables using the TinyTable library, often used in conjunction with data summary functions.

```javascript
var cellsToStyle = [
 // tinytable style arrays after
 {
  positions: [
   { i: '5', j: 3 },
   { i: '5', j: 4 }
  ],
  css_id: 'tinytable_css_a6zg274k5az00en2jgv6',
 },
 {
  positions: [
   { i: '1', j: 3 },
   { i: '2', j: 3 },
   { i: '3', j: 3 },
   { i: '4', j: 3 },
   { i: '1', j: 4 },
   { i: '2', j: 4 },
   { i: '3', j: 4 },
   { i: '4', j: 4 }
  ],
  css_id: 'tinytable_css_videyqr8vbeexrmgdlul',
 },
 {
  positions: [
   { i: '0', j: 3 },
   { i: '0', j: 4 }
  ],
  css_id: 'tinytable_css_175mn6g3v2akyu3hdek5',
 },
 {
  positions: [
   { i: '5', j: 1 },
   { i: '5', j: 2 }
  ],
  css_id: 'tinytable_css_j7c70zqhmuzdsfs99lep',
 },
 {
  positions: [
   { i: '1', j: 1 },
   { i: '2', j: 1 },
   { i: '3', j: 1 },
   { i: '4', j: 1 },
   { i: '1', j: 2 },
   { i: '2', j: 2 },
   { i: '3', j: 2 },
   { i: '4', j: 2 }
  ],
  css_id: 'tinytable_css_ewz515q55w8a89u0kobc',
 },
 {
  positions: [
   { i: '0', j: 1 },
   { i: '0', j: 2 }
  ],
  css_id: 'tinytable_css_7lwhg9mesjccdybeeks8',
 },
];

// Loop over the arrays to style the cells
cellsToStyle.forEach(function (group) {
 group.positions.forEach(function (cell) {
 tableFns_1sc9fyazl0ad1ms158cr.styleCell(cell.i, cell.j, group.css_id);
 });
});
```

--------------------------------

### Control Table Appearance

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Provides examples of controlling the appearance of the generated tables, such as adding borders or adjusting spacing.

```r
library(modelsummary)

# Generate a model summary with table appearance options
modelsummary(mtcars, 
             table_border = "all", 
             row_sep = "none")

```

--------------------------------

### Summarizing Models

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/datasummary.html

Demonstrates how to use datasummary to summarize statistical models.

```r
fit1 <- lm(Sepal.Length ~ Sepal.Width, data = iris)
fit2 <- lm(Petal.Length ~ Petal.Width, data = iris)
modelsummary(list(fit1, fit2))
```

--------------------------------

### Create a Cross-Tabulation

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/datasummary.html

Use datasummary_crosstab to explore the association between two categorical variables. This example uses the penguins dataset.

```R
library(modelsummary)
url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv'
penguins <- read.csv(url, na.strings = "")

datasummary_crosstab(species ~ sex, data = penguins)
```

--------------------------------

### update_modelsummary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/update_modelsummary.html

Updates the modelsummary package and its dependencies to the latest available versions from R-Universe or CRAN. A session restart is required after installation.

```APIDOC
## update_modelsummary

### Description

Update `modelsummary` and its dependencies to the latest R-Universe or CRAN versions. The R session needs to be restarted after install.

### Usage

```r
update_modelsummary(source = "development")
```

### Arguments

*   **source** (character) - Specifies the source for updates. Defaults to "development".

### Details

This function is used to ensure you have the latest version of the `modelsummary` package and any associated dependencies. It is recommended to restart your R session after the update is complete for the changes to take effect.
```

--------------------------------

### Passing Arguments to Sandwich Functions

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/modelsummary.html

Shows how to pass arguments to the underlying `sandwich` package functions directly from `modelsummary`. This example specifies bootstrap replicates and a clustering variable.

```R
modelsummary(mod, vcov = "bootstrap", R = 1000, cluster = "country")
```

--------------------------------

### Generating Tables in Different Formats

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/datasummary.html

Illustrates how to output the summary tables in various formats, such as HTML, LaTeX, and Markdown.

```r
modelsummary(iris, output = "iris_summary.html")
modelsummary(iris, output = "iris_summary.tex")
modelsummary(iris, output = "iris_summary.md")
```

--------------------------------

### Custom Correlation Function

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_correlation.html

Define and use a custom correlation function with datasummary_correlation. This example uses the Kendall correlation method.

```r
# custom function
cor_fun <- function(x) cor(x, method = "kendall")
datasummary_correlation(dat, method = cor_fun)
```

--------------------------------

### Categorical Data Summary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_skim.html

Use datasummary_skim with type = "categorical" to get a summary of categorical variables in your dataset.

```R
datasummary_skim(dat, type = "categorical")
```

--------------------------------

### Apply a Theme

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Use the `theme_modelsummary()` function to apply a predefined theme to your reports. This function accepts theme names like 'basic', 'journal', 'bootstrap', 'flatly', 'cerulean', 'spacelab', 'default', 'minimal', 'modern', 'striped', 'sky', 'emerald', 'red', 'darkly', 'lumen', 'sandstone', 'yeti', 'boring', 'ruins', 'marron', 'nature', 'clean', 'delight', 'solid', 'dracula', 'gruvbox', 'dracula_light', 'gruvbox_light'.

```r
library(modelsummary)

theme_modelsummary(theme = "journal")
```

--------------------------------

### TinyTable Factory Function

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/inst/tinytest/_tinysnapshot/mathmode-null.html

Creates table-specific functions using the TinyTable external factory. This is a setup step before styling cells.

```javascript
const tableFns_m4thvn8u3cwpmj8hiuwk = TinyTable.createTableFunctions("tinytable_m4thvn8u3cwpmj8hiuwk");
```

--------------------------------

### Keep Coefficients by Substring (Negative Lookahead)

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/modelsummary.html

Keep coefficients that start with a substring using a negative lookahead. All goodness-of-fit statistics are omitted.

```R
modelsummary(models, coef_omit = "^(?!Vol)", gof_omit = ".*")
```

--------------------------------

### Example of coef_omit with multiple substrings

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/modelsummary.html

Illustrates omitting coefficients that match either 'ei' or 'rc' using a regular expression with `coef_omit`.

```R
"ei|rc"
```

--------------------------------

### Set Table Backend for Specific Formats

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/datasummary_balance.html

Specify the table-making package to use for different output formats like HTML, Word, PNG, and LaTeX.

```R
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’)
```

--------------------------------

### Example of gof_map with list of lists for formatting

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/modelsummary.html

Provides detailed control over goodness-of-fit statistics formatting using a list of lists. Each inner list specifies 'raw', 'clean', and 'fmt' (which can be a string, number, or function).

```R
list(list(raw = "r.squared", clean = "R-Squared", fmt = "%.3f"))
```

--------------------------------

### Example of coef_omit with specific term omission

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/modelsummary.html

Demonstrates omitting a specific coefficient named 'Volume' using a regular expression with `coef_omit`.

```R
"^Volume$"
```

--------------------------------

### Customizing Table Appearance with `columns`

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/appearance.html

Shows how to select and order the columns to be displayed in the summary table using the `columns` argument. This allows for precise control over the table's structure.

```r
modelsummary(fit,
  columns = c("term", "estimate", "p.value"))
```

--------------------------------

### Example of coef_omit with regular expression

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/man/modelsummary.html

Shows how to use a regular expression with `coef_omit` to omit coefficients matching a specific substring, such as 'ei'.

```R
"ei"
```

--------------------------------

### Add custom statistics to model summary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/get_started.html

Include additional custom statistics in your model summary. This example adds the R-squared value.

```r
modelsummary(fit, 
 statistic = "conf.int", 
 add_rows = data.frame(
 "R-squared" = glance(fit)$r.squared,
 check_rows = "r.squared"
 ) 
)
```

--------------------------------

### Generate Data Summary

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/get_started.html

Use the `datasummary_skim` function to get a quick overview of the loaded dataset. This provides summary statistics for each variable.

```R
datasummary_skim(dat)
```

--------------------------------

### Load Libraries and Data

Source: https://github.com/vincentarelbundock/modelsummary/blob/main/docs/vignettes/get_started.html

Load necessary R packages and download a dataset from the Rdatasets archive. Prepare the data by creating a binary variable and selecting relevant columns.

```R
library(modelsummary)
library(tinytable)

url <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
dat <- read.csv(url) 
dat$Small <- dat$Pop1831 > median(dat$Pop1831)
dat <- dat[, 
  c("Donations", "Literacy", "Commerce", "Crime_pers", "Crime_prop", "Clergy", "Small")
]
```