| coffee_data | Sample dataset from a course about analysis of factorial... |
| describe_nonlinear | Describe the smooth term (for GAMs) or non-linear predictors |
| dot-uniroot.all | Copied from rootSolve package |
| efc | Sample dataset from the EFC Survey |
| estimate_contrasts | Estimate Marginal Contrasts |
| estimate_expectation | Model-based predictions |
| estimate_grouplevel | Group-specific parameters of mixed models random effects |
| estimate_means | Estimate Marginal Means (Model-based average at each factor... |
| estimate_slopes | Estimate Marginal Effects |
| fish | Sample data set |
| get_emmeans | Consistent API for 'emmeans' and 'marginaleffects' |
| modelbased-options | Global options from the modelbased package |
| modelbased-package | modelbased: Estimation of Model-Based Predictions, Contrasts... |
| pool_contrasts | Pool contrasts and comparisons from 'estimate_contrasts()' |
| pool_predictions | Pool Predictions and Estimated Marginal Means |
| print.estimate_contrasts | Printing modelbased-objects |
| puppy_love | More puppy therapy data |
| reexports | Objects exported from other packages |
| residualize_over_grid | Compute partial residuals from a data grid |
| smoothing | Smoothing a vector or a time series |
| visualisation_recipe.estimate_predicted | Automated plotting for 'modelbased' objects |
| zero_crossings | Find zero-crossings and inversion points |
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