Title: | Framework for Easy Statistical Modeling, Visualization, and Reporting |
---|---|
Description: | A meta-package that installs and loads a set of packages from 'easystats' ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching 'easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming. |
Authors: | Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Dominique Makowski [aut] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Mattan S. Ben-Shachar [aut] , Indrajeet Patil [aut] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Brenton M. Wiernik [aut] (<https://orcid.org/0000-0001-9560-6336>, @bmwiernik), Etienne Bacher [aut] , Rémi Thériault [aut] (<https://orcid.org/0000-0003-4315-6788>, @rempsyc) |
Maintainer: | Daniel Lüdecke <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.7.3.2 |
Built: | 2024-11-01 10:18:01 UTC |
Source: | https://github.com/easystats/easystats |
List all packages in the easystats ecosystem
easystats_packages()
easystats_packages()
A character vector
easystats_packages()
easystats_packages()
Update easystats-packages and its dependencies from CRAN, if necessary.
easystats_update(which = c("all", "core", "deps"))
easystats_update(which = c("all", "core", "deps"))
which |
String, indicates whether easystats-packages ( |
Invisible NULL
.
# check which local easystats-packages (and their dependencies) # are out of date and install updates from CRAN easystats_update() # update only easystats core-packages easystats_update("core")
# check which local easystats-packages (and their dependencies) # are out of date and install updates from CRAN easystats_update() # update only easystats core-packages easystats_update("core")
Welcome to the easyverse
easystats_zen()
easystats_zen()
A reassuring message.
easystats_zen()
easystats_zen()
This function can be used to install all the easystats packages, either latest development versions (from R-universe/GitHub) or the current versions from CRAN. If the development versions are installed, packages will be installed from the stable branch (master/main) for each package.
install_latest( source = c("development", "cran"), packages = "all", force = FALSE, verbose = TRUE )
install_latest( source = c("development", "cran"), packages = "all", force = FALSE, verbose = TRUE )
source |
Character. Either |
packages |
Character vector, indicating which packages to be installed.
By default, the option |
force |
Logical, if |
verbose |
Toggle messages. |
Invisible NULL
.
# install latest development-version of easystats packages from # the r-universe repository, but only those packages that have newer # versions available install_latest() # install all latest development-version of easystats packages from # the r-universe repository, no matter whether local installations # are up to date or not. install_latest(force = TRUE)
# install latest development-version of easystats packages from # the r-universe repository, but only those packages that have newer # versions available install_latest() # install all latest development-version of easystats packages from # the r-universe repository, no matter whether local installations # are up to date or not. install_latest(force = TRUE)
In easystats
, we have a 0-dependency policy, which makes our packages
fairly light and fast to install. However, we rely on many many (many)
packages for testing (at least all the packages for functions that we
support) and some specific features. These "soft dependencies" can be
downloaded at once using this function. This will allow you to fully utilize
all of easystats' functionalities without errors.
install_suggested(package = "easystats") show_suggested(package = "easystats") show_reverse_dependencies(package = "easystats")
install_suggested(package = "easystats") show_suggested(package = "easystats") show_reverse_dependencies(package = "easystats")
package |
If |
To reduce the dependency load, 'easystats' packages by default will not
download all internally needed packages. It will ask the user to download
them only if they are needed. The current function can help install all
packages a given 'easystats' package might need. For example,
install_suggested("see")
. show_suggested()
is a convenient helper
to show the current list of suggested packages for each 'easystats'
package.
Useful only for its side-effect of installing the needed packages.
# download all suggested packages if (FALSE) { install_suggested("easystats") } # listing all reverse dependencies of easystats packages show_reverse_dependencies() # listing all soft/weak dependencies of easystats packages show_suggested()
# download all suggested packages if (FALSE) { install_suggested("easystats") } # listing all reverse dependencies of easystats packages show_reverse_dependencies() # listing all soft/weak dependencies of easystats packages show_suggested()
easystats
A dashboard containing the following details for the entered regression model:
tabular summary of parameter estimates
dot-and-whisker plot for parameter estimates
tabular summary of indices for the quality of model fit
collection of models for checking model assumptions
text report
model information table
model_dashboard( model, check_model_args = NULL, parameters_args = NULL, performance_args = NULL, output_file = "easydashboard.html", output_dir = getwd(), rmd_dir = system.file("templates/easydashboard.Rmd", package = "easystats"), quiet = FALSE, browse_html = interactive() )
model_dashboard( model, check_model_args = NULL, parameters_args = NULL, performance_args = NULL, output_file = "easydashboard.html", output_dir = getwd(), rmd_dir = system.file("templates/easydashboard.Rmd", package = "easystats"), quiet = FALSE, browse_html = interactive() )
model |
A regression model object. |
check_model_args |
A list of named arguments that are passed down to
|
parameters_args |
A list of named arguments that are passed down to
|
performance_args |
A list of named arguments that are passed down to
|
output_file |
A string specifying the file name in |
output_dir |
A string specifying the path to the output directory for
report in |
rmd_dir |
A string specifying the path to the directory containing the
RMarkdown template file. By default, package uses the template shipped with
the package installation ( |
quiet |
An option to suppress printing during rendering from knitr,
pandoc command line and others. To only suppress printing of the last
"Output created: " message, you can set |
browse_html |
A logical deciding if the rendered HTML should be opened
in the browser. Defaults to |
An HTML dashboard.
For models with many observations, or for more complex models in general,
generating the model assumptions plot might become very slow. One reason
is that the underlying graphic engine becomes slow for plotting many data
points. In such cases, setting the argument check_model_args = list(show_dots = FALSE)
might help. Furthermore, look at other arguments of ?performance::check_model
,
which can be set using check_model_args
, to increase performance (in
particular the check
-argument can help, to skip some unnecessary checks).
# define a regression model mod <- lm(wt ~ mpg, mtcars) # with default options model_dashboard(mod) # customizing 'parameters' output: standardize coefficients model_dashboard(mod, parameters_args = list(standardize = "refit")) # customizing 'performance' output: only show selected performance metrics model_dashboard(mod, performance_args = list(metrics = c("AIC", "RMSE"))) # customizing output of model assumptions plot: don't show dots (faster plot) model_dashboard(mod, check_model_args = list(show_dots = FALSE))
# define a regression model mod <- lm(wt ~ mpg, mtcars) # with default options model_dashboard(mod) # customizing 'parameters' output: standardize coefficients model_dashboard(mod, parameters_args = list(standardize = "refit")) # customizing 'performance' output: only show selected performance metrics model_dashboard(mod, performance_args = list(metrics = c("AIC", "RMSE"))) # customizing output of model assumptions plot: don't show dots (faster plot) model_dashboard(mod, check_model_args = list(show_dots = FALSE))