# Easy Access to Model Information for Various Model Objects

## Documentation for package ‘insight’ version 1.5.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G H I L M N O P S T V

## -- A --

all_models_equal | Checks if all objects are models of same class
all_models_same_class | Checks if all objects are models of same class

## -- B --

broom_columns | Easystats columns

## -- C --

check_if_installed | Checking if needed package is installed
clean_names | Get clean names of model terms
clean_names.character | Get clean names of model terms
clean_parameters | Get clean names of model parameters
color_if | Color-formatting for data columns based on condition
color_text | Coloured console output
color_theme | Coloured console output
colour_if | Color-formatting for data columns based on condition
colour_text | Coloured console output
compact_character | Remove empty strings from character
compact_list | Remove empty elements from lists

## -- D --

display | Generic export of data frames into formatted tables
display.data.frame | Generic export of data frames into formatted tables
download_model | Download circus models

## -- E --

easystats_columns | Easystats columns
efc_insight | Sample dataset from the EFC Survey
ellipsis_info | Gather information about objects in ellipsis (dot dot dot)
ellipsis_info.default | Gather information about objects in ellipsis (dot dot dot)
export_table | Data frame and Tables Pretty Formatting

## -- F --

find_algorithm | Find sampling algorithm and optimizers
find_auxiliary | Find auxiliary (distributional) parameters from models
find_auxiliary.default | Find auxiliary (distributional) parameters from models
find_formula | Find model formula
find_formula.default | Find model formula
find_formula.nestedLogit | Find model formula
find_interactions | Find interaction terms from models
find_offset | Find possible offset terms in a model
find_parameters | Find names of model parameters
find_parameters.averaging | Find model parameters from models with special components
find_parameters.betamfx | Find names of model parameters from marginal effects models
find_parameters.BGGM | Find names of model parameters from Bayesian models
find_parameters.brmsfit | Find names of model parameters from Bayesian models
find_parameters.default | Find names of model parameters
find_parameters.emmGrid | Find model parameters from estimated marginal means objects
find_parameters.gam | Find names of model parameters from generalized additive models
find_parameters.gamlss | Find names of model parameters from generalized additive models
find_parameters.glmmTMB | Find names of model parameters from mixed models
find_parameters.zeroinfl | Find names of model parameters from zero-inflated models
find_predictors | Find names of model predictors
find_predictors.default | Find names of model predictors
find_random | Find names of random effects
find_random_slopes | Find names of random slopes
find_response | Find name of the response variable
find_response.joint | Find name of the response variable
find_smooth | Find smooth terms from a model object
find_statistic | Find statistic for model
find_terms | Find all model terms
find_terms.default | Find all model terms
find_transformation | Find possible transformation of model variables
find_transformation.default | Find possible transformation of model variables
find_variables | Find names of all variables
find_weights | Find names of model weights
fish | Sample data set for count models
format_alert | Format messages and warnings
format_bf | Bayes Factor formatting
format_capitalize | Capitalizes the first letter in a string
format_ci | Confidence/Credible Interval (CI) Formatting
format_ci.numeric | Confidence/Credible Interval (CI) Formatting
format_error | Format messages and warnings
format_message | Format messages and warnings
format_number | Convert number to words
format_p | p-values formatting
format_pd | Probability of direction (pd) formatting
format_percent | Numeric Values Formatting
format_rope | Percentage in ROPE formatting
format_string | String Values Formatting
format_string.character | String Values Formatting
format_table | Parameter table formatting
format_value | Numeric Values Formatting
format_value.data.frame | Numeric Values Formatting
format_value.numeric | Numeric Values Formatting
format_warning | Format messages and warnings
formula_ok | Find model formula

## -- G --

get_auxiliary | Get auxiliary parameters from models
get_call | Get the model's function call
get_correlation_slopes | Get variance components from random effects models
get_correlation_slope_intercept | Get variance components from random effects models
get_data | Get the data that was used to fit the model
get_data.afex_aov | Get the data that was used to fit the model
get_data.default | Get the data that was used to fit the model
get_data.glmmTMB | Get the data that was used to fit the model
get_data.rma | Get the data that was used to fit the model
get_datagrid | Create a reference grid
get_datagrid.data.frame | Create a reference grid
get_datagrid.default | Create a reference grid
get_datagrid.emmGrid | Extract a reference grid from objects created by '{emmeans}' and '{marginaleffects}'
get_datagrid.factor | Create a reference grid
get_datagrid.numeric | Create a reference grid
get_deviance | Model Deviance
get_deviance.default | Model Deviance
get_df | Extract degrees of freedom
get_df.default | Extract degrees of freedom
get_dispersion | Get auxiliary parameters from models
get_dispersion.default | Get auxiliary parameters from models
get_family | A robust alternative to stats::family
get_intercept | Get the value at the intercept
get_loglikelihood | Log-Likelihood and Log-Likelihood correction
get_loglikelihood.lm | Log-Likelihood and Log-Likelihood correction
get_loglikelihood_adjustment | Log-Likelihood and Log-Likelihood correction
get_mixed_info | Extract various information from mixed models
get_mixed_info.default | Extract various information from mixed models
get_mixed_info.glmmTMB | Extract various information from mixed models
get_model | Get a model objects that is saved as attribute
get_modelmatrix | Model Matrix
get_parameters | Get model parameters
get_parameters.betamfx | Get model parameters from marginal effects models
get_parameters.betareg | Get model parameters from models with special components
get_parameters.BFBayesFactor | Get model parameters from Bayesian models
get_parameters.BGGM | Get model parameters from Bayesian models
get_parameters.brmsfit | Get model parameters from Bayesian models
get_parameters.default | Get model parameters
get_parameters.emmGrid | Get model parameters from estimated marginal means objects
get_parameters.gamm | Get model parameters from generalized additive models
get_parameters.glmmTMB | Get model parameters from mixed models
get_parameters.htest | Get model parameters from htest-objects
get_parameters.zeroinfl | Get model parameters from zero-inflated and hurdle models
get_predicted | Model predictions (robust) and their confidence intervals
get_predicted.default | Model predictions (robust) and their confidence intervals
get_predicted.gam | Model predictions (robust) and their confidence intervals
get_predicted.lm | Model predictions (robust) and their confidence intervals
get_predicted.lmerMod | Model predictions (robust) and their confidence intervals
get_predicted.nestedLogit | Model predictions (robust) and their confidence intervals
get_predicted.principal | Model predictions (robust) and their confidence intervals
get_predicted.stanreg | Model predictions (robust) and their confidence intervals
get_predicted_ci | Confidence intervals around predicted values
get_predicted_ci.default | Confidence intervals around predicted values
get_predictors | Get the data from model predictors
get_priors | Get summary of priors used for a model
get_priors.brmsfit | Get summary of priors used for a model
get_random | Get the data from random effects
get_residuals | Extract model residuals
get_residuals.default | Extract model residuals
get_response | Get the values from the response variable
get_response.default | Get the values from the response variable
get_response.nestedLogit | Get the values from the response variable
get_sigma | Get residual standard deviation from models
get_simulated | Simulate values from fitted models
get_simulated.data.frame | Simulate values from fitted models
get_simulated.default | Simulate values from fitted models
get_simulated.glmmTMB | Simulate values from fitted models
get_simulated.lm | Simulate values from fitted models
get_simulated.merMod | Simulate values from fitted models
get_statistic | Get statistic associated with estimates
get_statistic.default | Get statistic associated with estimates
get_statistic.emmGrid | Get statistic associated with estimates
get_statistic.gam | Get statistic associated with estimates
get_statistic.gee | Get statistic associated with estimates
get_statistic.glmmTMB | Get statistic associated with estimates
get_transformation | Return function of transformed response variables
get_varcov | Get variance-covariance matrix from models
get_varcov.aov | Get variance-covariance matrix from models
get_varcov.default | Get variance-covariance matrix from models
get_varcov.glmgee | Get variance-covariance matrix from models
get_varcov.hurdle | Get variance-covariance matrix from models
get_varcov.mixor | Get variance-covariance matrix from models
get_variance | Get variance components from random effects models
get_variance.glmmTMB | Get variance components from random effects models
get_variance.merMod | Get variance components from random effects models
get_variance_dispersion | Get variance components from random effects models
get_variance_distribution | Get variance components from random effects models
get_variance_fixed | Get variance components from random effects models
get_variance_intercept | Get variance components from random effects models
get_variance_random | Get variance components from random effects models
get_variance_residual | Get variance components from random effects models
get_variance_slope | Get variance components from random effects models
get_weights | Get the values from model weights
get_weights.default | Get the values from model weights

## -- H --

has_intercept | Checks if model has an intercept
has_single_value | Small helper functions

## -- I --

is_bayesian_model | Checks if a model is a Bayesian model
is_converged | Convergence test for mixed effects models
is_converged.merMod | Convergence test for mixed effects models
is_empty_object | Check if object is empty
is_gam_model | Checks if a model is a generalized additive model
is_mixed_model | Checks if a model is a mixed effects model
is_model | Checks if an object is a regression model or statistical test object
is_model_supported | Checks if a regression model object is supported by the insight package
is_multivariate | Checks if an object stems from a multivariate response model
is_nested_models | Checks whether a list of models are nested models
is_nullmodel | Checks if model is a null-model (intercept-only)
is_regression_model | Checks if an object is a regression model or statistical test object

## -- L --

link_function | Get link-function from model object
link_function.betamfx | Get link-function from model object
link_function.betareg | Get link-function from model object
link_function.DirichletRegModel | Get link-function from model object
link_function.gamlss | Get link-function from model object
link_inverse | Get link-inverse function from model object
link_inverse.betamfx | Get link-inverse function from model object
link_inverse.betareg | Get link-inverse function from model object
link_inverse.DirichletRegModel | Get link-inverse function from model object
link_inverse.gamlss | Get link-inverse function from model object
loglikelihood | Log-Likelihood and Log-Likelihood correction

## -- M --

model_info | Access information from model objects
model_info.brmsfit | Access information from model objects
model_info.default | Access information from model objects
model_name | Name the model
model_name.default | Name the model

## -- N --

null_model | Compute intercept-only model for regression models
null_model.default | Compute intercept-only model for regression models
n_grouplevels | Count number of random effect levels in a mixed model
n_obs | Get number of observations from a model
n_obs.afex_aov | Get number of observations from a model
n_obs.glm | Get number of observations from a model
n_obs.stanmvreg | Get number of observations from a model
n_obs.svyolr | Get number of observations from a model
n_parameters | Count number of parameters in a model
n_parameters.default | Count number of parameters in a model
n_parameters.glmmTMB | Count number of parameters in a model
n_parameters.merMod | Count number of parameters in a model
n_unique | Small helper functions
n_unique.default | Small helper functions

## -- O --

object_has_names | Check names and rownames
object_has_rownames | Check names and rownames

## -- P --

print_color | Coloured console output
print_colour | Coloured console output
print_html | Generic export of data frames into formatted tables
print_html.data.frame | Generic export of data frames into formatted tables
print_md | Generic export of data frames into formatted tables
print_md.data.frame | Generic export of data frames into formatted tables
print_parameters | Prepare summary statistics of model parameters for printing

## -- S --

safe_deparse | Small helper functions
safe_deparse_symbol | Small helper functions
standardize_column_order | Standardize column order
standardize_column_order.parameters_model | Standardize column order
standardize_names | Standardize column names
standardize_names.parameters_model | Standardize column names
supported_models | Checks if a regression model object is supported by the insight package

## -- T --

text_remove_backticks | Remove backticks from a string
text_remove_backticks.data.frame | Remove backticks from a string
trim_ws | Small helper functions
trim_ws.data.frame | Small helper functions

## -- V --

validate_argument | Validate arguments against a given set of options