Estimation of Model-Based Predictions, Contrasts and Means

Documentation for package ‘modelbased’ version 0.1.2

as.numeric_ifnumeric	Convert to Numeric if Possible
estimate_contrasts	Estimate contrasts between factor levels
estimate_contrasts.lm	Estimate contrasts
estimate_contrasts.stanreg	Estimate contrasts
estimate_link	Generates predictions
estimate_link.data.frame	Generates predictions for Bayesian models
estimate_link.glm	Generates predictions for Frequentist models
estimate_link.stanreg	Generates predictions for Bayesian models
estimate_means	Estimate average value of response variable at each factor levels
estimate_means.lm	Estimate marginal means
estimate_means.stanreg	Estimate marginal means
estimate_response	Generates predictions
estimate_response.data.frame	Generates predictions for Bayesian models
estimate_response.glm	Generates predictions for Frequentist models
estimate_response.stanreg	Generates predictions for Bayesian models
estimate_slopes	Estimate the slopes of a numeric predictor (over different factor levels)
estimate_slopes.lm	Estimate the slopes of a numeric predictor (over different factor levels)
estimate_slopes.stanreg	Estimate the slopes of a numeric predictor (over different factor levels)
estimate_smooth	Describe the smooth term (for GAMs) or non-linear predictors
estimate_smooth.stanreg	Describe the smooth term (for GAMs) or non-linear predictors
find_inversions	Find points of inversion
reshape_draws	Reshape estimations with Bayesian posterior draws to long format
smoothing	Smoothing a vector or a time series
visualisation_matrix	Create a reference grid
zero_crossings	Find zero crossings of a vector