Title: Transform Models into 'LaTeX' Equations
Version: 0.3.7
Maintainer: Philippe Grosjean <phgrosjean@sciviews.org>
Description: The goal of 'equatiomatic' is to reduce the pain associated with writing 'LaTeX' formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model.
License: CC BY 4.0
Depends: R (≥ 3.3.0)
URL: https://github.com/datalorax/equatiomatic, https://datalorax.github.io/equatiomatic/
BugReports: https://github.com/datalorax/equatiomatic/issues
Imports: broom (≥ 0.7.0), broom.mixed, shiny, knitr, stats, utils
Suggests: covr, shinyWidgets, forecast (≥ 8.13), ggplot2 (≥ 3.3.3), latex2exp (≥ 0.4.0), lme4, MASS, ordinal, rmarkdown, testthat (≥ 3.0.0), texPreview (≥ 2.0.0), gtsummary, spelling
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Config/testthat/edition: 3
Config/testthat/parallel: true
Language: en-US
NeedsCompilation: no
Packaged: 2025-07-04 08:13:21 UTC; phgrosjean
Author: Daniel Anderson ORCID iD [aut], Andrew Heiss ORCID iD [aut], Jay Sumners [aut], Joshua Rosenberg ORCID iD [ctb], Jonathan Sidi ORCID iD [ctb], Ellis Hughes ORCID iD [ctb], Thomas Fung ORCID iD [ctb], Reza Norouzian ORCID iD [ctb], Indrajeet Patil ORCID iD [ctb], Quinn White ORCID iD [ctb], David Kane [ctb], Philippe Grosjean ORCID iD [cre], Teun van den Brand ORCID iD [ctb], Guyliann Engels ORCID iD [ctb]
Repository: CRAN
Date/Publication: 2025-07-04 09:00:06 UTC

equatiomatic: Transform Models into 'LaTeX' Equations

Description

logo

The goal of 'equatiomatic' is to reduce the pain associated with writing 'LaTeX' formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding 'LaTeX' code for the model.

Author(s)

Maintainer: Philippe Grosjean phgrosjean@sciviews.org (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Arrest data from Gelman & Hill

Description

Arrest data from Gelman & Hill's book, used in Chapter 6 (and others). The data have been aggregated by precinct and race/ethnicity, with the sum of prior arrests and stops calculated. You can download the original data here: http://www.stat.columbia.edu/~gelman/arm/examples/police/

Usage

arrests

Format

A tibble with 225 rows and 4 variables:

precinct

An integer denoting the precinct identification number.

eth

A factor with the coded race/ethnicity

stops

The number of police stops

arrests

The number of prior arrests (this is used as an offset variable in the book)

'LaTeX' equation for R models

Description

Extract the variable names from a model to produce a 'LaTeX' equation. Supports any model where there is a broom::tidy() method. This is a generic function with methods for lmerMod objects obtained with lme4::lmer(), glmerMod objects with lme4::glmer(), forecast_ARIMA with forecast::Arima() and default, with the later further covering most "base" R models implemented in broom::tidy() like lm objects with stats::lm(), glm objects with stats::glm() or polr objects with MASS::polr(). The default method also supports clm objects obtained with ordinal::clm().

Usage

extract_eq(
  model,
  intercept = "alpha",
  greek = "beta",
  greek_colors = NULL,
  subscript_colors = NULL,
  var_colors = NULL,
  var_subscript_colors = NULL,
  raw_tex = FALSE,
  swap_var_names = NULL,
  swap_subscript_names = NULL,
  ital_vars = FALSE,
  label = NULL,
  index_factors = FALSE,
  show_distribution = FALSE,
  wrap = FALSE,
  terms_per_line = 4,
  operator_location = "end",
  align_env = "aligned",
  use_coefs = FALSE,
  coef_digits = 2,
  fix_signs = TRUE,
  font_size = NULL,
  mean_separate = NULL,
  return_variances = FALSE,
  se_subscripts = FALSE,
  ...
)

Arguments

model

A fitted model

intercept

How should the intercept be displayed? Default is "alpha", but can also accept "beta", in which case the it will be displayed as beta zero.

greek

What notation should be used for coefficients? Currently only accepts "beta" (with plans for future development). Can be used in combination with raw_tex to use any notation, e.g., "\hat{\beta}".

greek_colors

The colors of the greek notation in the equation. Must be a single color (named or HTML hex code) or a vector of colors (which will be recycled if smaller than the number of terms in the model). When rendering to PDF, I suggest using HTML hex codes, as not all named colors are recognized by LaTeX, but equatiomatic will internally create the color definitions for you if HTML codes are supplied. Note that this is not yet implemented for mixed effects models (lme4).

subscript_colors

The colors of the subscripts for the greek notation. The argument structure is equivalent to greek_colors (i.e., see above for more detail).

var_colors

The color of the variable names. This takes a named vector of the form c("variable" = "color"). For example c("bill_length_mm" = "#00d4fa", "island" = "#00fa85"). Colors can be names (e.g., "red") or HTML hex codes, as shown in the example.

var_subscript_colors

The colors of the factor subscripts for categorical variables. The interface for this is equivalent to var_colors, and all subscripts for a given variable will be displayed in the provided color. For example, the code c("island" = "green") would result in the subscripts for "Dream" and "Torgersen" being green (assuming "Biscoe" was the reference group).

raw_tex

Logical. Is the greek code being passed to denote coefficients raw tex code?

swap_var_names

A vector of the form c("old_var_name" = "new name"). For example: c("bill_length_mm" = "Bill Length (MM)").

swap_subscript_names

A vector of the form c("old_subscript_name" = "new name"). For example: c("f" = "Female").

ital_vars

Logical, defaults to FALSE. Should the variable names not be wrapped in the \operatorname{} command?

label

A label for the equation, which can then be used for in-text references. See example here. Note that this only works for PDF output. The in-text references also must match the label exactly, and must be formatted as \ref{eq: label}, where label is a place holder for the specific label. Notice the space after the colon before the label. This also must be there, or the cross-reference will fail.

index_factors

Logical, defaults to FALSE. Should the factors be indexed, rather than using subscripts to display all levels?

show_distribution

Logical. When fitting a logistic or probit regression, should the binomial distribution be displayed? Defaults to FALSE.

wrap

Logical, defaults to FALSE. Should the terms on the right-hand side of the equation be split into multiple lines? This is helpful with models with many terms.

terms_per_line

Integer, defaults to 4. The number of right-hand side terms to include per line. Used only when wrap is TRUE.

operator_location

Character, one of “end” (the default) or “start”. When terms are split across multiple lines, they are split at mathematical operators like +. If set to “end”, each line will end with a trailing operator (+ or -). If set to “start”, each line will begin with an operator.

align_env

TeX environment to wrap around equation. Must be one of aligned, aligned*, align, or align*. Defaults to aligned.

use_coefs

Logical, defaults to FALSE. Should the actual model estimates be included in the equation instead of math symbols?

coef_digits

Integer, defaults to 2. The number of decimal places to round to when displaying model estimates.

fix_signs

Logical, defaults to TRUE. If disabled, coefficient estimates that are negative are preceded with a "+" (e.g. 5(x) + -3(z)). If enabled, the "+ -" is replaced with a "-" (e.g. 5(x) - 3(z)).

font_size

The font size of the equation. Defaults to default of the output format. Takes any of the standard LaTeX arguments (see here).

mean_separate

Currently only support for lmer models. Should the mean structure be inside or separated from the normal distribution? Defaults to NULL, in which case it will become TRUE if there are more than three fixed-effect parameters. If TRUE, the equation will be displayed as, for example, outcome ~ N(mu, sigma); mu = alpha + beta_1(wave). If FALSE, this same equation would be outcome ~ N(alpha + beta, sigma).

return_variances

Logical. When use_coefs = TRUE with a mixed effects model (e.g., lme4::lmer()), should the variances and co-variances be returned? If FALSE (the default) standard deviations and correlations are returned instead.

se_subscripts

Logical. If se_subscripts = TRUE then the equation will include the standard errors below each coefficient. This is supported for lm and glm models.

...

Additional arguments (for future development; not currently used).

Details

The different methods all use the same arguments, but not all arguments are suitable to all models. Check here above to determine if a feature is implemented for a given model.

Value

A character of class “equation”.

Examples

# Simple model
mod1 <- lm(mpg ~ cyl + disp, mtcars)
extract_eq(mod1)

# Include all variables
mod2 <- lm(mpg ~ ., mtcars)
extract_eq(mod2)

# Works for categorical variables too, putting levels as subscripts
data("penguins", package = "equatiomatic")
mod3 <- lm(body_mass_g ~ bill_length_mm + species, penguins)
extract_eq(mod3)

set.seed(8675309)
d <- data.frame(
  cat1 = rep(letters[1:3], 100),
  cat2 = rep(LETTERS[1:3], each = 100),
  cont1 = rnorm(300, 100, 1),
  cont2 = rnorm(300, 50, 5),
  out = rnorm(300, 10, 0.5)
)
mod4 <- lm(out ~ ., d)
extract_eq(mod4)

# Don't italicize terms
extract_eq(mod1, ital_vars = FALSE)

# Wrap equations in an "aligned" environment
extract_eq(mod2, wrap = TRUE)

# Wider equation wrapping
extract_eq(mod2, wrap = TRUE, terms_per_line = 4)

# Include model estimates instead of Greek letters
extract_eq(mod2, wrap = TRUE, terms_per_line = 2, use_coefs = TRUE)

# Don't fix doubled-up "+ -" signs
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, use_coefs = TRUE, fix_signs = FALSE)

# Use indices for factors instead of subscripts
extract_eq(mod2, wrap = TRUE, terms_per_line = 4, index_factors = TRUE)

# Use other model types, like glm
set.seed(8675309)
d <- data.frame(
  out = sample(0:1, 100, replace = TRUE),
  cat1 = rep(letters[1:3], 100),
  cat2 = rep(LETTERS[1:3], each = 100),
  cont1 = rnorm(300, 100, 1),
  cont2 = rnorm(300, 50, 5)
)
mod5 <- glm(out ~ ., data = d, family = binomial(link = "logit"))
extract_eq(mod5, wrap = TRUE)

Format 'LaTeX' equations

Description

Format 'LaTeX' equations built with extract_eq.

Usage

## S3 method for class 'equation'
format(x, ..., latex = knitr::is_latex_output())

Arguments

x

'LaTeX' equation built with extract_eq

...

not used

latex

Logical, whether the output is LaTeX or not. The default value uses knitr::is_latex_output() to determine the current output format.

Value

A character string with the equation formatted either as proper LaTeX code, or as a display equation tag (surrounded by ⁠$$...$$⁠) for R Markdown or Quarto documents.

A subset of the full 1982 High School and Beyond Survey

Description

This is the dataset used throughout Raudenbush & Bryk (2002).

Usage

hsb

Format

A tibble with 7185 rows and 8 variables:

sch.id

An integer denoting the school identification number. There are 160 unique schools.

math

Individual students' math score.

size

The number of students in the school.

sector

A dummy variable (integer) denoting whether the school is public (sector = 0) or catholic (sector = 1). There are 90 public schools and 70 catholic.

meanses

A group-mean centered SES variable at the school level

minority

A dummy variable indicating if the student was coded as white (minority = 0) or not (minority = 1).

female

A dummy variable indicating if the student was coded as female (female = 1) or not (female = 0).

ses

A student-level composite variable indicating the students' socio-economic status.

Print 'LaTeX' equations in R Markdown environments

Description

Print 'LaTeX' equations built with extract_eq nicely in R Markdown environments.

Usage

## S3 method for class 'equation'
knit_print(
  x,
  ...,
  tex_packages = "\\renewcommand*\\familydefault{\\rmdefault}"
)

Arguments

x

'LaTeX' equation built with extract_eq

...

not used

tex_packages

A string with LaTeX code to include in the header, usually to include LaTeX packages in the output.

Value

A string with the equation formatted according to R Markdown's output format (different output for HTML, PDF, docx, gfm, markdown_strict). The format is detected automatically, so, you do not have to worry about it.

Size measurements for adult foraging penguins near Palmer Station, Antarctica

Description

Data originally from palmerpenguins. Includes measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

Usage

penguins

Format

A tibble with 344 rows and 8 variables:

species

a factor denoting penguin species (Adélie, Chinstrap and Gentoo)

island

a factor denoting island in Palmer Archipelago, Antarctica (Biscoe, Dream or Torgersen)

bill_length_mm

a number denoting bill length (millimeters)

bill_depth_mm

a number denoting bill depth (millimeters)

flipper_length_mm

an integer denoting flipper length (millimeters)

body_mass_g

an integer denoting body mass (grams)

sex

a factor denoting penguin sex (female, male)

year

an integer denoting the study year (2007, 2008, or 2009)

Source

Adélie penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Adélie penguins (Pygoscelis adeliae) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative doi:10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f

Gentoo penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Gentoo penguin (Pygoscelis papua) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative doi:10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689

Chinstrap penguins: Palmer Station Antarctica LTER and K. Gorman. 2020. Structural size measurements and isotopic signatures of foraging among adult male and female Chinstrap penguin (Pygoscelis antarcticus) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 6. Environmental Data Initiative doi:10.6073/pasta/c14dfcfada8ea13a17536e73eb6fbe9e

Originally published in: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081

The polls data from Gelman and Hill ()

Description

This is the dataset used in Gelman & Hill's book, Data Analysis Using Regression and Multilevel/Hierarchical Models. They are polling data on the presidential election from 1988, collected one week before the election. You can download all the data from the book here: http://www.stat.columbia.edu/~gelman/arm/examples/ARM_Data.zip. Note that this is only a few of the variables from the original data supplied with the book.

Usage

polls

Format

A tibble with 13,544 rows and 7 variables:

state

An integer denoting the state identification number.

edu

An ordered factor stating the education level of the respondent

age

An unordered factor stating the age of range of the respondent

female

A dummy variable (integer) denoting whether the respondent was coded as male (female = 0) or female (female = 1).

black

A dummy variable (integer) denoting whether the respondent was coded as Black (black = 1) or not Black (black = 0).

weight

A sampling weight

bush

Whether the respondent stated they were in favor of voting for George Bush Sr.

Print 'LaTeX' equations

Description

Print 'LaTeX' equations built with extract_eq.

Usage

## S3 method for class 'equation'
print(x, ...)

Arguments

x

'LaTeX' equation built with extract_eq

...

not used

Value

The unmodified object 'x' is returned invisibly. The function is used for its side effect of printing the equation.

Display equations in shiny apps

Description

[Experimental] These are a set of functions designed to help render equations in Shiny applications (see the vignette about Shiny).

Usage

renderEq(expr, env = parent.frame(), quoted = FALSE, outputArgs = list())

eqOutput(outputId)

Arguments

expr

An R expression, specifically a call to extract_eq()

env

The environment

quoted

Is the expresion quoted?

outputArgs

list of output arguments

outputId

The identifier of the output from the server. Should be passed as a string.

Value

Render the equation in a suitable way for Shiny for renderEq() in an eqOutput() equation output element that can be included in a panel.

Functions

Simulated longitudinal data

Description

Data are simulated to be similar to longitudinal data collected within schools/districts.

Usage

sim_longitudinal

Format

A tibble with 1000 rows and 8 variables:

sid

An integer denoting the individual student. There are 100 students.

school

An integer denoting the school There are 15 schools.

district

An integer denoting the school district. There are 5 districts.

group

A character variable denoting the instructional level of the student, low, medium, or high.

treatment

A factor indicating whether the student received the intervention treatment (0 = no treatment received; 1 = treatment received).

prop_low

The proportion of student in the school in the low instructional group.

wave

The assessment wave. Each student has nine waves of data collection

score

The individual students' score at the given wave.

Simple simulated time series data

Description

Output from set.seed(42); simple_ts <- ts(rnorm(1000),freq = 4). This is included primarily for unit testing.

Usage

simple_ts

Format

A tibble with 1000 rows and 8 variables:

Qtr1

First quarter simulated values.

Qtr2

Second quarter simulated values.

Qtr3

Third quarter simulated values.

Qtr4

Fourth quarter simulated values.

Simulated data for time-series regression

Description

Output from set.seed(42); ts_reg_list <- list(x1 = rnorm(1000), x2 = rnorm(1000), ts_rnorm = rnorm(1000)).

Usage

ts_reg_list

Format

A tibble with 1000 rows and 8 variables:

x1

Random normal simulated data.

x2

Random normal simulated data.

ts_rnorm

Random normal simulated data.