Package index • modelbased

Model-based Estimations
`describe_nonlinear()` `estimate_smooth()`	Describe the smooth term (for GAMs) or non-linear predictors
`estimate_contrasts()`	Estimate Marginal Contrasts
`estimate_expectation()` `estimate_link()` `estimate_prediction()` `estimate_relation()`	Model-based predictions
`estimate_grouplevel()` `reshape_grouplevel()`	Group-specific parameters of mixed models random effects
`estimate_means()`	Estimate Marginal Means (Model-based average at each factor level)
`estimate_slopes()`	Estimate Marginal Effects
Data grids and Visualisation
`plot(<estimate_predicted>)` `plot(<estimate_means>)` `tinyplot(<estimate_means>)` `visualisation_recipe(<estimate_predicted>)` `visualisation_recipe(<estimate_slopes>)` `visualisation_recipe(<estimate_grouplevel>)`	Automated plotting for 'modelbased' objects
`residualize_over_grid()`	Compute partial residuals from a data grid
Missing Data
`pool_contrasts()`	Pool contrasts and comparisons from `estimate_contrasts()`
`pool_predictions()` `pool_slopes()`	Pool Predictions and Estimated Marginal Means
Miscellaenous
`display(<estimate_contrasts>)` `print(<estimate_contrasts>)`	Printing modelbased-objects
`describe_nonlinear()` `estimate_smooth()`	Describe the smooth term (for GAMs) or non-linear predictors
`get_emcontrasts()` `get_emmeans()` `get_emtrends()` `get_marginalcontrasts()` `get_marginalmeans()` `get_marginaltrends()`	Consistent API for 'emmeans' and 'marginaleffects'
`zero_crossings()` `find_inversions()`	Find zero-crossings and inversion points
`smoothing()`	Smoothing a vector or a time series
Global options
`modelbased-options`	Global options from the modelbased package
Sample datasets
`coffee_data`	Sample dataset from a course about analysis of factorial designs
`puppy_love`	More puppy therapy data
`efc`	Sample dataset from the EFC Survey
`fish`	Sample data set

Reference

Model-based Estimations

Data grids and Visualisation

Missing Data

Miscellaenous

Global options

Sample datasets