Estimation of Model-Based Predictions, Contrasts and Means


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Documentation for package ‘modelbased’ version 0.5.1

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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.glmmTMB 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