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

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

pool_contrasts(): Pool contrasts and comparisons from estimate_contrasts()
pool_predictions() pool_slopes(): Pool Predictions and Estimated Marginal Means

as.data.frame(<estimate_contrasts>): Converting modelbased-objects into raw data frames
display(<estimate_contrasts>) format(<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

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