Help for package report

Type:

Package

Title:

Automated Reporting of Results and Statistical Models

Version:

0.6.1

Maintainer:

Rémi Thériault <remi.theriault@mail.mcgill.ca>

Description:

The aim of the 'report' package is to bridge the gap between R’s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.

License:

MIT + file LICENSE

URL:

https://easystats.github.io/report/

BugReports:

https://github.com/easystats/report/issues

Depends:

R (≥ 3.6)

Imports:

bayestestR (≥ 0.15.0), effectsize (≥ 1.0.0), insight (≥ 1.0.1), parameters (≥ 0.24.1), performance (≥ 0.13.0), datawizard (≥ 1.0.0), stats, tools, utils

Suggests:

BayesFactor, brms, collapse, ivreg, knitr, lavaan, lme4, dplyr, Formula, rmarkdown, rstanarm, survival, modelbased (≥ 0.9.0), emmeans, marginaleffects (≥ 0.25.0), RcppEigen, BH, testthat (≥ 3.2.1)

VignetteBuilder:

knitr

Encoding:

UTF-8

Language:

en-US

RoxygenNote:

7.3.2

Config/testthat/edition:

Config/Needs/website:

rstudio/bslib, r-lib/pkgdown, easystats/easystatstemplate

Collate:

'cite_easystats.R' 'format_algorithm.R' 'format_citation.R' 'format_formula.R' 'format_model.R' 'reexports.R' 'report-package.R' 'report.BFBayesFactor.R' 'utils_combine_tables.R' 'report.lm.R' 'report.MixMod.R' 'report_text.R' 'report.R' 'report.htest.R' 'report.aov.R' 'report.bayesfactor_models.R' 'report.lme4.R' 'report.stanreg.R' 'report.brmsfit.R' 'report.character.R' 'report.compare.loo.R' 'report.compare_performance.R' 'report.coxph.R' 'report.data.frame.R' 'report.default.R' 'report.estimate_contrasts.R' 'report.factor.R' 'report.glm.R' 'report.glmmTMB.R' 'report.ivreg.R' 'report.lavaan.R' 'report.lme.R' 'report.numeric.R' 'report.sessionInfo.R' 'report.survreg.R' 'report.test_performance.R' 'report.zeroinfl.R' 'report_effectsize.R' 'report_htest_chi2.R' 'report_htest_cor.R' 'report_htest_fisher.R' 'report_htest_friedman.R' 'report_htest_kruskal.R' 'report_htest_ttest.R' 'report_htest_wilcox.R' 'report_info.R' 'report_intercept.R' 'report_misc.R' 'report_model.R' 'report_parameters.R' 'report_participants.R' 'report_performance.R' 'report_priors.R' 'report_random.R' 'report_s.R' 'report_sample.R' 'report_statistics.R' 'report_table.R' 'utils_error_message.R' 'utils_grouped_df.R' 'utils_misspelled_variables.R' 'zzz.R'

NeedsCompilation:

Packaged:

2025-02-07 01:08:17 UTC; coding

Author:

Dominique Makowski

[aut], Daniel Lüdecke

[aut], Indrajeet Patil

[aut], Rémi Thériault

[aut, cre], Mattan S. Ben-Shachar

[aut], Brenton M. Wiernik

[aut], Rudolf Siegel

[ctb], Camden Bock

[ctb]

Repository:

CRAN

Date/Publication:

2025-02-07 08:10:02 UTC

report: Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption

Description

report’s primary goal is to bridge the gap between R’s output and the formatted results contained in your manuscript. It automatically produces reports of models and data frames according to best practices guidelines (e.g., APA’s style), ensuring standardization and quality in results reporting.

Details

report-package

Author(s)

Maintainer: Rémi Thériault remi.theriault@mail.mcgill.ca (ORCID)

Authors:

Dominique Makowski dom.makowski@gmail.com (ORCID)
Daniel Lüdecke d.luedecke@uke.de (ORCID)
Indrajeet Patil patilindrajeet.science@gmail.com (ORCID)
Mattan S. Ben-Shachar matanshm@post.bgu.ac.il (ORCID)
Brenton M. Wiernik brenton@wiernik.org (ORCID)

Other contributors:

Rudolf Siegel mutlusun@users.noreply.github.com (ORCID) [contributor]
Camden Bock camden.bock@maine.edu (ORCID) [contributor]

Create or test objects of class report.

Description

Allows to create or test whether an object is of the report class.

Usage

as.report_text(x, ...)

as.report(text, table = NULL, plot = NULL, ...)

is.report(x)

as.report_effectsize(x, summary = NULL, prefix = "  - ", ...)

as.report_info(x, summary = NULL, ...)

as.report_intercept(x, summary = NULL, ...)

as.report_model(x, summary = NULL, ...)

as.report_parameters(x, summary = NULL, prefix = "  - ", ...)

as.report_performance(x, summary = NULL, ...)

as.report_priors(x, summary = NULL, ...)

as.report_random(x, summary = NULL, ...)

as.report_statistics(x, summary = NULL, prefix = "  - ", ...)

as.report_table(x, ...)

Arguments

x

An arbitrary R object.

...

Args to be saved as attributes.

text

Text obtained via report_text()

table

Table obtained via report_table()

plot

Plot obtained via report_plot(). Not yet implemented.

summary

Add a summary as attribute (to be extracted via summary()).

prefix

The prefix to be displayed in front of each parameter.

Value

A report object or a TRUE/FALSE value.

Cite the easystats ecosystem

Description

A convenient function for those who wish to cite the easystats packages.

Usage

cite_easystats(
  packages = "easystats",
  format = c("text", "markdown", "biblatex"),
  intext_prefix = TRUE,
  intext_suffix = "."
)

## S3 method for class 'cite_easystats'
summary(object, what = "all", ...)

## S3 method for class 'cite_easystats'
print(x, what = "all", ...)

Arguments

packages

A character vector of packages to cite. Can be "all" for all easystats pacakges or a vector with specific package names.

format

The format to generate citations. Can be "text" for plain text, "markdown" for markdown citations and CSL bibliography (recommended for writing in RMarkdown), or "biblatex" for BibLaTeX citations and bibliography.

intext_prefix

A character vector of length 1 containing text to include before in-text citations. If TRUE, defaults to "Analyses were conducted using the easystats collection of packages ". If FALSE or NA, no prefix is included.

intext_suffix

A character vector of length 1 containing text to include after in-text citations. Defaults to ".". If FALSE or NA, no suffix is included.

what

What elements of the citations to print, can be "all", "intext", or "refs".

...

Not used. Included for compatibility with the generic function.

x, object

A "cite_easystats" object to print.

Value

A list of class "cite_easystats" with elements:

intext: In-text citations in the requested format
refs: References or bibliography in the requested format

Examples


# Cite just the 'easystats' umbrella package:
cite_easystats()
summary(cite_easystats(), what = "all")

# Cite every easystats package:
cite_easystats(packages = "all")
summary(cite_easystats(packages = "all"), what = "all")

# Cite specific packages:
cite_easystats(packages = c("modelbased", "see"))
summary(cite_easystats(packages = c("modelbased", "see")), what = "all")

# To cite easystats packages in an RMarkdown document, use:

## In-text citations:
print(cite_easystats(format = "markdown"), what = "intext")

## Bibliography (print with the  `output = 'asis'` option on the code chunk)
print(cite_easystats(format = "markdown"), what = "refs")

Convenient formatting of text components

Description

Convenient formatting of text components

Usage

format_algorithm(x)

format_formula(x, what = "conditional")

format_model(x)

Arguments

x

The R object that you want to report (see list of of supported objects above).

what

The name of the item returned by insight::find_formula.

Value

A character string.

Examples

model <- lm(Sepal.Length ~ Species, data = iris)
format_algorithm(model)


# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1 | Species), data = iris)
format_algorithm(model)

model <- lm(Sepal.Length ~ Species, data = iris)
format_formula(model)


# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1 | Species), data = iris)
format_formula(model)
format_formula(model, "random")

model <- lm(Sepal.Length ~ Species, data = iris)
format_model(model)


# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1 | Species), data = iris)
format_model(model)

Citation formatting

Description

Convenience functions to manipulate and format citations. Only works with APA formatted citations, for now.

Usage

format_citation(citation, authorsdate = FALSE, short = FALSE, intext = FALSE)

cite_citation(citation)

clean_citation(citation)

Arguments

citation

A character string of a citation.

authorsdate

Only show authors and date (remove title, journal, etc.).

short

If more than one authors, replace by ⁠et al.⁠

intext

Remove brackets around the date (so that it can be placed inside larger parentheses).

Value

A character string.

Examples

library(report)

citation <- "Makowski, D., Ben-Shachar, M. S., Patil, I., & Ludecke, D. (2020).
Methods and Algorithms for Correlation Analysis in R. Journal of Open Source
Software, 5(51), 2306."

format_citation(citation, authorsdate = TRUE)
format_citation(citation, authorsdate = TRUE, short = TRUE)
format_citation(citation, authorsdate = TRUE, short = TRUE, intext = TRUE)

cite_citation(citation)
clean_citation(citation())

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

insight: display, print_html, print_md

Automatic reporting of R objects

Description

Create reports of different objects. See the documentation for your object's class:

Usage

report(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Details

System and packages (sessionInfo)
Dataframes and vectors
Correlations and t-tests (htest)
ANOVAs (⁠aov, anova, aovlist, ...⁠)
Regression models (⁠glm, lm, ...⁠)
Mixed models (⁠glmer, lmer, glmmTMB, ...⁠)
Bayesian models (⁠stanreg, brms...⁠)
Bayes factors (from bayestestR)
Structural Equation Models (SEM) (from lavaan)
Model comparison (from performance())

Most of the time, the object created by the report() function can be further transformed, for instance summarized (using summary()), or converted to a table (using as.data.frame()).

Organization

report_table and report_text are the two distal representations of a report, and are the two provided in report(). However, intermediate steps are accessible (depending on the object) via specific functions (e.g., report_parameters).

Output

The report() function generates a report-object that contain in itself different representations (e.g., text, tables, plots). These different representations can be accessed via several functions, such as:

as.report_text(r): Detailed text.
as.report_text(r, summary=TRUE): Minimal text giving the minimal information.
as.report_table(r): Comprehensive table including most available indices.
as.report_table(r, summary=TRUE): Minimal table.

Note that for some report objects, some of these representations might be identical.

Value

A list-object of class report, which contains further list-objects with a short and long description of the model summary, as well as a short and long table of parameters and fit indices.

Examples


library(report)

model <- t.test(mtcars$mpg ~ mtcars$am)
r <- report(model)

# Text
r
summary(r)

# Tables
as.data.frame(r)
summary(as.data.frame(r))

Reporting `BFBayesFactor` objects from the `BayesFactor` package

Description

Interpretation of the Bayes factor output from the BayesFactor package.

Usage

## S3 method for class 'BFBayesFactor'
report(x, h0 = "H0", h1 = "H1", ...)

## S3 method for class 'BFBayesFactor'
report_statistics(x, table = NULL, ...)

Arguments

x

An object of class BFBayesFactor.

h0, h1

Names of the null and alternative hypotheses.

...

Other arguments to be passed to effectsize::interpret_bf and insight::format_bf.

table

A parameters table (this argument is meant for internal use).

Examples



library(BayesFactor)

rez <- BayesFactor::ttestBF(iris$Sepal.Width, iris$Sepal.Length)
report_statistics(rez, exact = TRUE) # Print exact BF
report(rez, h0 = "the null hypothesis", h1 = "the alternative")

rez <- BayesFactor::correlationBF(iris$Sepal.Width, iris$Sepal.Length)
report(rez)

Reporting ANOVAs

Description

Create reports for ANOVA models.

Usage

## S3 method for class 'aov'
report(x, ...)

## S3 method for class 'aov'
report_effectsize(x, ...)

## S3 method for class 'aov'
report_table(x, ...)

## S3 method for class 'aov'
report_statistics(x, table = NULL, ...)

## S3 method for class 'aov'
report_parameters(x, ...)

## S3 method for class 'aov'
report_model(x, table = NULL, ...)

## S3 method for class 'aov'
report_info(x, effectsize = NULL, ...)

## S3 method for class 'aov'
report_text(x, table = NULL, ...)

Arguments

x

Object of class aov, anova or aovlist.

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

effectsize

Provide the output of report_effectsize() to avoid its re-computation.

Value

An object of class report().

Examples

data <- iris
data$Cat1 <- rep(c("A", "B"), length.out = nrow(data))

model <- aov(Sepal.Length ~ Species * Cat1, data = data)
r <- report(model)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

Reporting Models' Bayes Factor

Description

Create reports of Bayes factors for model comparison.

Usage

## S3 method for class 'bayesfactor_models'
report(
  x,
  interpretation = "jeffreys1961",
  exact = TRUE,
  protect_ratio = TRUE,
  ...
)

## S3 method for class 'bayesfactor_inclusion'
report(
  x,
  interpretation = "jeffreys1961",
  exact = TRUE,
  protect_ratio = TRUE,
  ...
)

Arguments

x

Object of class bayesfactor_inclusion.

interpretation

Effect size interpretation set of rules (see interpret_bf).

exact

Should very large or very small values be reported with a scientific format (e.g., 4.24e5), or as truncated values (as "> 1000" and "< 1/1000").

protect_ratio

Should values smaller than 1 be represented as ratios?

...

Arguments passed to or from other methods.

Value

An object of class report().

Examples


library(bayestestR)
# Bayes factor - models
mo0 <- lm(Sepal.Length ~ 1, data = iris)
mo1 <- lm(Sepal.Length ~ Species, data = iris)
mo2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
mo3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0)

r <- report(BFmodels)
r

# Bayes factor - inclusion
inc_bf <- bayesfactor_inclusion(BFmodels, prior_odds = c(1, 2, 3), match_models = TRUE)

r <- report(inc_bf)
r
as.data.frame(r)

Reporting Bayesian Models from brms

Description

Create reports for Bayesian models. The description of the parameters follows the Sequential Effect eXistence and sIgnificance Testing framework (see SEXIT documentation).

Usage

## S3 method for class 'brmsfit'
report(x, ...)

Arguments

x

Object of class lm or glm.

...

Arguments passed to or from other methods.

Value

An object of class report().

Examples



# Bayesian models
library(brms)
model <- suppressWarnings(brm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 300))
r <- report(model, verbose = FALSE)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

Reporting Datasets and Dataframes

Description

Create reports for data frames.

Usage

## S3 method for class 'character'
report(
  x,
  n_entries = 3,
  levels_percentage = "auto",
  missing_percentage = "auto",
  ...
)

## S3 method for class 'data.frame'
report(
  x,
  n = FALSE,
  centrality = "mean",
  dispersion = TRUE,
  range = TRUE,
  distribution = FALSE,
  levels_percentage = "auto",
  digits = 2,
  n_entries = 3,
  missing_percentage = "auto",
  ...
)

## S3 method for class 'factor'
report(x, levels_percentage = "auto", ...)

## S3 method for class 'numeric'
report(
  x,
  n = FALSE,
  centrality = "mean",
  dispersion = TRUE,
  range = TRUE,
  distribution = FALSE,
  missing_percentage = "auto",
  digits = 2,
  ...
)

Arguments

x

The R object that you want to report (see list of of supported objects above).

n_entries

Number of different character entries to show. Can be "all".

levels_percentage

Show characters entries and factor levels by number or percentage. If "auto", then will be set to number and percentage if the length if n observations larger than 100.

missing_percentage

Show missing by number (default) or percentage. If "auto", then will be set to number and percentage if the length if n observations larger than 100.

...

Arguments passed to or from other methods.

n

Include number of observations for each individual variable.

centrality

Character vector, indicating the index of centrality (either "mean" or "median").

dispersion

Show index of dispersion (sd if centrality = "mean", or mad if centrality = "median").

range

Show range.

distribution

Show kurtosis and skewness.

digits

Number of significant digits.

Value

An object of class report().

Examples

r <- report(iris,
  centrality = "median", dispersion = FALSE,
  distribution = TRUE, missing_percentage = TRUE
)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))


# grouped analysis using `{dplyr}` package
library(dplyr)
r <- iris %>%
  group_by(Species) %>%
  report()
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

Reporting Bayesian Model Comparison

Description

Automatically report the results of Bayesian model comparison using the loo package.

Usage

## S3 method for class 'compare.loo'
report(x, include_IC = TRUE, include_ENP = FALSE, ...)

Arguments

x

An object of class brms::loo_compare.

include_IC

Whether to include the information criteria (IC).

include_ENP

Whether to include the effective number of parameters (ENP).

...

Additional arguments (not used for now).

Details

The rule of thumb is that the models are "very similar" if |elpd_diff| (the absolute value of elpd_diff) is less than 4 (Sivula, Magnusson and Vehtari, 2020). If superior to 4, then one can use the SE to obtain a standardized difference (Z-diff) and interpret it as such, assuming that the difference is normally distributed. The corresponding p-value is then calculated as 2 * pnorm(-abs(Z-diff)). However, note that if the raw ELPD difference is small (less than 4), it doesn't make much sense to rely on its standardized value: it is not very useful to conclude that a model is much better than another if both models make very similar predictions.

Value

Objects of class report_text().

Examples



library(brms)

m1 <- brms::brm(mpg ~ qsec, data = mtcars)
m2 <- brms::brm(mpg ~ qsec + drat, data = mtcars)
m3 <- brms::brm(mpg ~ qsec + drat + wt, data = mtcars)

x <- suppressWarnings(brms::loo_compare(
  brms::add_criterion(m1, "loo"),
  brms::add_criterion(m2, "loo"),
  brms::add_criterion(m3, "loo"),
  model_names = c("m1", "m2", "m3")
))
report(x)
report(x, include_IC = FALSE)
report(x, include_ENP = TRUE)

Reporting models comparison

Description

Create reports for model comparison as obtained by the performance::compare_performance() function in the performance package.

Usage

## S3 method for class 'compare_performance'
report(x, ...)

## S3 method for class 'compare_performance'
report_table(x, ...)

## S3 method for class 'compare_performance'
report_statistics(x, table = NULL, ...)

## S3 method for class 'compare_performance'
report_parameters(x, table = NULL, ...)

## S3 method for class 'compare_performance'
report_text(x, table = NULL, ...)

Arguments

x

Object of class ⁠NEW OBJECT⁠.

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

Value

An object of class report().

Examples


library(report)
library(performance)

m1 <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
m2 <- lm(Sepal.Length ~ Petal.Length + Species, data = iris)
m3 <- lm(Sepal.Length ~ Petal.Length, data = iris)

x <- performance::compare_performance(m1, m2, m3)
r <- report(x)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

# Specific reports
report_table(x)
report_statistics(x)
report_parameters(x)

Template to add report support for new objects

Description

Template file to add report support for new objects. Check-out the vignette on Supporting New Models.

Usage

## Default S3 method:
report(x, ...)

## Default S3 method:
report_effectsize(x, ...)

## Default S3 method:
report_table(x, ...)

## Default S3 method:
report_statistics(x, ...)

## Default S3 method:
report_parameters(x, ...)

## Default S3 method:
report_intercept(x, ...)

## Default S3 method:
report_model(x, ...)

## Default S3 method:
report_random(x, ...)

## Default S3 method:
report_priors(x, ...)

## Default S3 method:
report_performance(x, ...)

## Default S3 method:
report_info(x, ...)

## Default S3 method:
report_text(x, ...)

Arguments

x

Object of class ⁠NEW OBJECT⁠.

...

Arguments passed to or from other methods.

Value

An object of class report().

Examples

library(report)

# Add a reproducible example instead of the following
model <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
r <- report(model)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

Reporting `estimate_contrasts` objects

Description

Create reports for estimate_contrasts objects.

Usage

## S3 method for class 'estimate_contrasts'
report(x, ...)

## S3 method for class 'estimate_contrasts'
report_table(x, ...)

## S3 method for class 'estimate_contrasts'
report_text(x, table = NULL, ...)

Arguments

x

Object of class estimate_contrasts.

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

Value

An object of class report().

Examples


library(modelbased)
model <- lm(Sepal.Width ~ Species, data = iris)
contr <- estimate_contrasts(model)
report(contr)

Reporting `htest` objects (Correlation, t-test...)

Description

Create reports for htest objects (t.test(), cor.test(), etc.).

Usage

## S3 method for class 'htest'
report(x, ...)

## S3 method for class 'htest'
report_effectsize(x, ...)

## S3 method for class 'htest'
report_table(x, ...)

## S3 method for class 'htest'
report_statistics(x, table = NULL, ...)

## S3 method for class 'htest'
report_parameters(x, table = NULL, ...)

## S3 method for class 'htest'
report_model(x, table = NULL, ...)

## S3 method for class 'htest'
report_info(x, effectsize = NULL, ...)

## S3 method for class 'htest'
report_text(x, table = NULL, ...)

Arguments

x

Object of class htest.

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

effectsize

Provide the output of report_effectsize() to avoid its re-computation.

Value

An object of class report().

Examples

# t-tests
report(t.test(iris$Sepal.Width, iris$Sepal.Length))
report(t.test(iris$Sepal.Width, iris$Sepal.Length, var.equal = TRUE))
report(t.test(mtcars$mpg ~ mtcars$vs))
report(t.test(mtcars$mpg, mtcars$vs, paired = TRUE), verbose = FALSE)
report(t.test(iris$Sepal.Width, mu = 1))

# Correlations
report(cor.test(iris$Sepal.Width, iris$Sepal.Length))

Reports of Structural Equation Models (SEM)

Description

Create a report for lavaan objects.

Usage

## S3 method for class 'lavaan'
report(x, ...)

## S3 method for class 'lavaan'
report_performance(x, table = NULL, ...)

Arguments

x

Object of class lavaan.

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

Value

An object of class report().

Examples



# Structural Equation Models (SEM)
library(lavaan)
structure <- "ind60 =~ x1 + x2 + x3
              dem60 =~ y1 + y2 + y3
              dem60 ~ ind60"
model <- lavaan::sem(structure, data = PoliticalDemocracy)
r <- report(model)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

# Specific reports
suppressWarnings(report_table(model))
suppressWarnings(report_performance(model))

Reporting (General) Linear Models

Description

Create reports for (general) linear models.

Usage

## S3 method for class 'lm'
report(x, include_effectsize = TRUE, effectsize_method = "refit", ...)

## S3 method for class 'lm'
report_effectsize(x, effectsize_method = "refit", ...)

## S3 method for class 'lm'
report_table(x, include_effectsize = TRUE, ...)

## S3 method for class 'lm'
report_statistics(
  x,
  table = NULL,
  include_effectsize = TRUE,
  include_diagnostic = TRUE,
  ...
)

## S3 method for class 'lm'
report_parameters(
  x,
  table = NULL,
  include_effectsize = TRUE,
  include_intercept = TRUE,
  ...
)

## S3 method for class 'lm'
report_intercept(x, table = NULL, ...)

## S3 method for class 'lm'
report_model(x, table = NULL, ...)

## S3 method for class 'lm'
report_performance(x, table = NULL, ...)

## S3 method for class 'lm'
report_info(
  x,
  effectsize = NULL,
  include_effectsize = FALSE,
  parameters = NULL,
  ...
)

## S3 method for class 'lm'
report_text(x, table = NULL, ...)

## S3 method for class 'merMod'
report_random(x, ...)

Arguments

x

Object of class lm or glm.

include_effectsize

If FALSE, won't include effect-size related indices (standardized coefficients, etc.).

effectsize_method

See documentation for effectsize::effectsize().

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

include_diagnostic

If FALSE, won't include diagnostic related indices for Bayesian models (ESS, Rhat).

include_intercept

If FALSE, won't include the intercept.

effectsize

Provide the output of report_effectsize() to avoid its re-computation.

parameters

Provide the output of report_parameters() to avoid its re-computation.

Value

An object of class report().

Examples


library(report)

# Linear models
model <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
r <- report(model)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

# Logistic models
model <- glm(vs ~ disp, data = mtcars, family = "binomial")
r <- report(model)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r <- report(model)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

Report R environment (packages, system, etc.)

Description

Report R environment (packages, system, etc.)

Usage

## S3 method for class 'sessionInfo'
report(x, ...)

report_packages(session = NULL, include_R = TRUE, ...)

cite_packages(session = NULL, include_R = TRUE, ...)

report_system(session = NULL)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

session

A sessionInfo object.

include_R

Include R in the citations.

Value

For report_packages, a data frame of class with information on package name, version and citation.

An object of class report().

Examples


library(report)

session <- sessionInfo()

r <- report(session)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

# Convenience functions
report_packages(include_R = FALSE)
cite_packages(prefix = "> ")
report_system()

Reporting Bayesian Models

Description

Create reports for Bayesian models. The description of the parameters follows the Sequential Effect eXistence and sIgnificance Testing framework (see SEXIT documentation).

Usage

## S3 method for class 'stanreg'
report(x, ...)

Arguments

x

Object of class lm or glm.

...

Arguments passed to or from other methods.

Value

An object of class report().

Examples



# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 500))
r <- report(model)
r
summary(r)
as.data.frame(r)

Reporting models comparison

Description

Create reports for model comparison as obtained by the performance::compare_performance() function in the performance package.

Usage

## S3 method for class 'test_performance'
report(x, ...)

## S3 method for class 'test_performance'
report_table(x, ...)

## S3 method for class 'test_performance'
report_statistics(x, table = NULL, ...)

## S3 method for class 'test_performance'
report_parameters(x, table = NULL, ...)

## S3 method for class 'test_performance'
report_text(x, table = NULL, ...)

Arguments

x

Object of class ⁠NEW OBJECT⁠.

...

Arguments passed to or from other methods.

table

Provide the output of report_table() to avoid its re-computation.

Value

An object of class report().

Examples


library(report)
library(performance)

m1 <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
m2 <- lm(Sepal.Length ~ Petal.Length + Species, data = iris)
m3 <- lm(Sepal.Length ~ Petal.Length, data = iris)

x <- performance::test_performance(m1, m2, m3)
r <- report(x)
r
summary(r)
as.data.frame(r)
summary(as.data.frame(r))

# Specific reports
report_table(x)
report_statistics(x)
report_parameters(x)

Miscellaneous reports

Description

Other convenient or totally useless reports.

Usage

report_date(...)

report_story(...)

Arguments

...

Arguments passed to or from other methods.

Value

Objects of class report_text().

Examples

library(report)

report_date()
summary(report_date())
report_story()

Report the effect size(s) of a model or a test

Description

Computes, interpret and formats the effect sizes of a variety of models and statistical tests (see list of supported objects in report()).

Usage

report_effectsize(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_effectsize().

Examples

library(report)

# h-tests
report_effectsize(t.test(iris$Sepal.Width, iris$Sepal.Length))

# ANOVAs
report_effectsize(aov(Sepal.Length ~ Species, data = iris))

# GLMs
report_effectsize(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_effectsize(glm(vs ~ disp, data = mtcars, family = "binomial"))



# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_effectsize(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_effectsize(model, effectsize_method = "basic")

Report additional information

Description

Reports additional information relevant to the report (see list of supported objects in report()).

Usage

report_info(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_info().

Examples

library(report)

# h-tests
report_info(t.test(iris$Sepal.Width, iris$Sepal.Length))

# ANOVAs
report_info(aov(Sepal.Length ~ Species, data = iris))

# GLMs
report_info(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_info(lm(Sepal.Length ~ Petal.Length * Species, data = iris), include_effectsize = TRUE)
report_info(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_info(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 300))
report_info(model)

Report intercept

Description

Reports intercept of regression models (see list of supported objects in report()).

Usage

report_intercept(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_intercept().

Examples


library(report)

# GLMs
report_intercept(lm(Sepal.Length ~ Species, data = iris))
report_intercept(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_intercept(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_intercept(model)

Report the model type

Description

Reports the type of different R objects (see list of supported objects in report()).

Usage

report_model(x, table = NULL, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

table

A table obtained via report_table(). If not provided, will run it.

...

Arguments passed to or from other methods.

Value

A character string.

Examples


library(report)

# h-tests
report_model(t.test(iris$Sepal.Width, iris$Sepal.Length))

# ANOVA
report_model(aov(Sepal.Length ~ Species, data = iris))

# GLMs
report_model(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_model(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_model(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_model(model)

Report the parameters of a model

Description

Creates a list containing a description of the parameters of R objects (see list of supported objects in report()).

Usage

report_parameters(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

A vector.

Examples


library(report)

# Miscellaneous
r <- report_parameters(sessionInfo())
r
summary(r)

# Data
report_parameters(iris$Sepal.Length)
report_parameters(as.character(round(iris$Sepal.Length, 1)))
report_parameters(iris$Species)
report_parameters(iris)

# h-tests
report_parameters(t.test(iris$Sepal.Width, iris$Sepal.Length))

# ANOVA
report_parameters(aov(Sepal.Length ~ Species, data = iris))

# GLMs
report_parameters(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_parameters(lm(Petal.Width ~ Species, data = iris), include_intercept = FALSE)
report_parameters(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_parameters(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_parameters(model)

Reporting the participant data

Description

A helper function to help you format the participants data (age, sex, ...) in the participants section.

Usage

report_participants(
  data,
  age = NULL,
  sex = NULL,
  gender = NULL,
  education = NULL,
  country = NULL,
  race = NULL,
  participants = NULL,
  by = NULL,
  spell_n = FALSE,
  digits = 1,
  threshold = 10,
  group = NULL,
  ...
)

Arguments

data

A data frame.

age

The name of the column containing the age of the participant.

sex

The name of the column containing the sex of the participant. The classes should be one of c("Male", "M", "Female", "F"). Note that you can specify other characters here as well (e.g., "Intersex"), but the function will group all individuals in those groups as "Other".

gender

The name of the column containing the gender of the classes should be one of ⁠c("Man", "M", "Male", Woman", "W", "F", "Female", Non-Binary", "N")⁠. Note that you can specify other characters here as well (e.g., "Gender Fluid"), but the function will group all individuals in those groups as "Non-Binary".

education

The name of the column containing education information.

country

The name of the column containing country information.

race

The name of the column containing race/ethnicity information.

participants

The name of the participants' identifier column (for instance in the case of repeated measures).

by

A character vector indicating the name(s) of the column(s) used for stratified description.

spell_n

Logical, fully spell the sample size ("Three participants" instead of "3 participants").

digits

Number of significant digits.

threshold

Percentage after which to combine, e.g., countries (default is 10%, so countries that represent less than 10% will be combined in the "other" category).

group

Deprecated. Use by instead.

...

Arguments passed to or from other methods.

Value

A character vector with description of the "participants", based on the information provided in data.

Examples

library(report)
data <- data.frame(
  "Age" = c(22, 23, 54, 21, 8, 42),
  "Sex" = c("Intersex", "F", "M", "M", "NA", NA),
  "Gender" = c("N", "W", "W", "M", "NA", NA)
)
report_participants(data, age = "Age", sex = "Sex")

# Years of education (relative to high school graduation)
data$Education <- c(0, 8, -3, -5, 3, 5)
report_participants(data,
  age = "Age", sex = "Sex", gender = "Gender",
  education = "Education"
)

# Education as factor
data$Education2 <- c(
  "Bachelor", "PhD", "Highschool",
  "Highschool", "Bachelor", "Bachelor"
)
report_participants(data, age = "Age", sex = "Sex", gender = "Gender", education = "Education2")

# Country
data <- data.frame(
  "Age" = c(22, 23, 54, 21, 8, 42, 18, 32, 24, 27, 45),
  "Sex" = c("Intersex", "F", "F", "M", "M", "M", "F", "F", "F", "F", "F"),
  "Gender" = c("N", "W", "W", "M", "M", "M", "W", "W", "W", "W", "W"),
  "Country" = c(
    "USA", NA, "Canada", "Canada", "India", "Germany",
    "USA", "USA", "USA", "USA", "Canada"
  )
)
report_participants(data)

# Country, control presentation treshold
report_participants(data, threshold = 5)

# Race/ethnicity
data <- data.frame(
  "Age" = c(22, 23, 54, 21, 8, 42, 18, 32, 24, 27, 45),
  "Sex" = c("Intersex", "F", "F", "M", "M", "M", "F", "F", "F", "F", "F"),
  "Gender" = c("N", "W", "W", "M", "M", "M", "W", "W", "W", "W", "W"),
  "Race" = c(
    "Black", NA, "White", "Asian", "Black", "Arab", "Black",
    "White", "Asian", "Southeast Asian", "Mixed"
  )
)
report_participants(data)

# Race/ethnicity, control presentation treshold
report_participants(data, threshold = 5)

# Repeated measures data
data <- data.frame(
  "Age" = c(22, 22, 54, 54, 8, 8),
  "Sex" = c("I", "F", "M", "M", "F", "F"),
  "Gender" = c("N", "W", "W", "M", "M", "M"),
  "Participant" = c("S1", "S1", "s2", "s2", "s3", "s3")
)
report_participants(data, age = "Age", sex = "Sex", gender = "Gender", participants = "Participant")

# Grouped data
data <- data.frame(
  "Age" = c(22, 22, 54, 54, 8, 8, 42, 42),
  "Sex" = c("I", "I", "M", "M", "F", "F", "F", "F"),
  "Gender" = c("N", "N", "W", "M", "M", "M", "Non-Binary", "Non-Binary"),
  "Participant" = c("S1", "S1", "s2", "s2", "s3", "s3", "s4", "s4"),
  "Condition" = c("A", "A", "A", "A", "B", "B", "B", "B")
)

report_participants(data,
  age = "Age",
  sex = "Sex",
  gender = "Gender",
  participants = "Participant",
  by = "Condition"
)

# Spell sample size
paste(
  report_participants(data, participants = "Participant", spell_n = TRUE),
  "were recruited in the study by means of torture and coercion."
)

Report the model's quality and fit indices

Description

Investigating the fit of statistical models to data often involves selecting the best fitting model amongst many competing models. This function helps report indices of model fit for various models. Reports the type of different R objects . For a list of supported objects, see report()).

Usage

report_performance(x, table = NULL, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

table

A table obtained via report_table(). If not provided, will run it.

...

Arguments passed to or from other methods.

Value

An object of class report_performance().

Examples


# GLMs
report_performance(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_performance(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_performance(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_performance(model)




# Structural Equation Models (SEM)
library(lavaan)
structure <- "ind60 =~ x1 + x2 + x3
              dem60 =~ y1 + y2 + y3
              dem60 ~ ind60 "
model <- lavaan::sem(structure, data = PoliticalDemocracy)
suppressWarnings(report_performance(model))

Report priors of Bayesian models

Description

Reports priors of Bayesian models (see list of supported objects in report()).

Usage

report_priors(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_priors().

Examples



# Bayesian models
library(rstanarm)
model <- stan_glm(mpg ~ disp, data = mtcars, refresh = 0, iter = 1000)
r <- report_priors(model)
r
summary(r)

Report random effects and factors

Description

Reports random effects of mixed models (see list of supported objects in report()).

Usage

report_random(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_random().

Examples



# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r <- report_random(model)
r
summary(r)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_lmer(
  mpg ~ disp + (1 | cyl),
  data = mtcars, refresh = 0, iter = 1000
))
r <- report_random(model)
r
summary(r)




library(brms)
model <- suppressWarnings(brm(mpg ~ disp + (1 | cyl), data = mtcars, refresh = 0, iter = 1000))
r <- report_random(model)
r
summary(r)

Report S- and p-values in easy language.

Description

Reports interpretation of S- and p-values in easy language.

Usage

report_s(s = NULL, p = NULL, test_value = 0, test_parameter = "parameter")

Arguments

s

An S-value. Either s or p must be provided.

p

A p-value. Either s or p must be provided.

test_value

The value of the test parameter under the null hypothesis.

test_parameter

The name of the test parameter under the null hypothesis.

Value

A string with the interpretation of the S- or p-value.

Examples

report_s(s = 1.5)
report_s(p = 0.05)

Sample Description

Description

Create sample description table (also referred to as "Table 1").

Usage

report_sample(
  data,
  by = NULL,
  centrality = "mean",
  ci = NULL,
  ci_method = "wilson",
  ci_correct = FALSE,
  select = NULL,
  exclude = NULL,
  weights = NULL,
  total = TRUE,
  digits = 2,
  n = FALSE,
  group_by = NULL,
  ...
)

Arguments

data

A data frame for which descriptive statistics should be created.

by

Character vector, indicating the column(s) for possible grouping of the descriptive table. Note that weighting (see weights) does not work with more than one grouping column.

centrality

Character, indicates the statistics that should be calculated for numeric variables. May be "mean" (for mean and standard deviation) or "median" (for median and median absolute deviation) as summary.

ci

Level of confidence interval for relative frequencies (proportions). If not NULL, confidence intervals are shown for proportions of factor levels.

ci_method

Character, indicating the method how to calculate confidence intervals for proportions. Currently implemented methods are "wald" and "wilson". Note that "wald" can produce intervals outside the plausible range of [0, 1], and thus it is recommended to prefer the "wilson" method. The formulae for the confidence intervals are:

"wald":

p \pm z \sqrt{\frac{p (1 - p)}{n}}
"wilson":

\frac{2np + z^2 \pm z \sqrt{z^2 + 4npq}}{2(n + z^2)}

where p is the proportion (of a factor level), q is 1-p, z is the critical z-score based on the interval level and n is the length of the vector (cf. Newcombe 1998, Wilson 1927).

ci_correct

Logical, it TRUE, applies continuity correction. See Newcombe 1998 for different correction-methods based on the chosen ci_method.

select

Character vector, with column names that should be included in the descriptive table.

exclude

Character vector, with column names that should be excluded from the descriptive table.

weights

Character vector, indicating the name of a potential weight-variable. Reported descriptive statistics will be weighted by weight.

total

Add a Total column.

digits

Number of decimals.

n

Logical, actual sample size used in the calculation of the reported descriptive statistics (i.e., without the missing values).

group_by

Deprecated. Use by instead.

...

Arguments passed to or from other methods.

Value

A data frame of class report_sample with variable names and their related summary statistics.

References

Newcombe, R. G. (1998). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine. 17 (8): 857–872
Wilson, E. B. (1927). Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association. 22 (158): 209–212

Examples

library(report)

report_sample(iris[, 1:4])
report_sample(iris, select = c("Sepal.Length", "Petal.Length", "Species"))
report_sample(iris, by = "Species")
report_sample(airquality, by = "Month", n = TRUE, total = FALSE)

# confidence intervals for proportions
set.seed(123)
d <- data.frame(x = factor(sample(letters[1:3], 100, TRUE, c(0.01, 0.39, 0.6))))
report_sample(d, ci = 0.95, ci_method = "wald") # ups, negative CI
report_sample(d, ci = 0.95, ci_method = "wilson") # negative CI fixed
report_sample(d, ci = 0.95, ci_correct = TRUE) # continuity correction

Report the statistics of a model

Description

Creates a list containing a description of the parameters' values of R objects (see list of supported objects in report()). Useful to insert in parentheses in plots or reports.

Usage

report_statistics(x, table = NULL, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

table

A table obtained via report_table(). If not provided, will run it.

...

Arguments passed to or from other methods.

Value

An object of class report_statistics().

Examples


library(report)

# Data
report_statistics(iris$Sepal.Length)
report_statistics(as.character(round(iris$Sepal.Length, 1)))
report_statistics(iris$Species)
report_statistics(iris)

# h-tests
report_statistics(t.test(iris$Sepal.Width, iris$Sepal.Length))

# ANOVA
report_statistics(aov(Sepal.Length ~ Species, data = iris))

# GLMs
report_statistics(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_statistics(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_statistics(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_statistics(model)

Report a descriptive table

Description

Creates tables to describe different objects (see list of supported objects in report()).

Usage

report_table(x, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

Value

An object of class report_table().

Examples


# Miscellaneous
r <- report_table(sessionInfo())
r
summary(r)

# Data
report_table(iris$Sepal.Length)
report_table(as.character(round(iris$Sepal.Length, 1)))
report_table(iris$Species)
report_table(iris)

# h-tests
report_table(t.test(mtcars$mpg ~ mtcars$am))

# ANOVAs
report_table(aov(Sepal.Length ~ Species, data = iris))

# GLMs
report_table(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
report_table(glm(vs ~ disp, data = mtcars, family = "binomial"))




# Mixed models
library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_table(model)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(Sepal.Length ~ Species, data = iris, refresh = 0, iter = 600))
report_table(model, effectsize_method = "basic")




# Structural Equation Models (SEM)
library(lavaan)
structure <- "ind60 =~ x1 + x2 + x3
              dem60 =~ y1 + y2 + y3
              dem60 ~ ind60"
model <- lavaan::sem(structure, data = PoliticalDemocracy)
suppressWarnings(report_table(model))

Report a textual description of an object

Description

Creates text containing a description of the parameters of R objects (see list of supported objects in report()).

Usage

report_text(x, table = NULL, ...)

Arguments

x

The R object that you want to report (see list of of supported objects above).

table

A table obtained via report_table(). If not provided, will run it.

...

Arguments passed to or from other methods.

Value

An object of class report_text().

Examples

library(report)

# Miscellaneous
r <- report_text(sessionInfo())
r
summary(r)

# Data
report_text(iris$Sepal.Length)
report_text(as.character(round(iris$Sepal.Length, 1)))
report_text(iris$Species)
report_text(iris)

# h-tests
report_text(t.test(iris$Sepal.Width, iris$Sepal.Length))

# ANOVA
r <- report_text(aov(Sepal.Length ~ Species, data = iris))
r
summary(r)

# GLMs
r <- report_text(lm(Sepal.Length ~ Petal.Length * Species, data = iris))
r
summary(r)



library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r <- report_text(model)
r
summary(r)




# Bayesian models
library(rstanarm)
model <- suppressWarnings(stan_glm(mpg ~ cyl + wt, data = mtcars, refresh = 0, iter = 600))
r <- report_text(model)
r
summary(r)

report: Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption

Description

Details

Author(s)

See Also

Create or test objects of class report.

Description

Usage

Arguments

Value

Cite the easystats ecosystem

Description

Usage

Arguments

Value

Examples

Convenient formatting of text components

Description

Usage

Arguments

Value

Examples

Citation formatting

Description

Usage

Arguments

Value

Examples

Objects exported from other packages

Description

Automatic reporting of R objects

Description

Usage

Arguments

Details

Organization

Output

Value

See Also

Examples

Reporting BFBayesFactor objects from the BayesFactor package

Description

Usage

Arguments

Examples

Reporting ANOVAs

Description

Usage

Arguments

Value

See Also

Examples

Reporting Models' Bayes Factor

Description

Usage

Arguments

Value

See Also

Examples

Reporting Bayesian Models from brms

Description

Usage

Arguments

Value

See Also

Examples

Reporting Datasets and Dataframes

Description

Usage

Arguments

Value

Examples

Reporting Bayesian Model Comparison

Description

Usage

Arguments

Details

Value

Examples

Reporting models comparison

Reporting `BFBayesFactor` objects from the `BayesFactor` package

Reporting `estimate_contrasts` objects

Reporting `htest` objects (Correlation, t-test...)