modelbased 0.12.0
  • Reference
  • Introductions
    • Overview of Vignettes
    • Basics
    • Data grids
    • Marginal means
    • Contrast analysis
    • Marginal effects and derivatives
    • Mixed effects models
    • Interpretation
    • Use a model to make predictions
    • Interpret models using Effect Derivatives
    • Estimate and re-use Random Effects
    • Visualization
    • Plotting estimated marginal means
    • Visualize effects and interactions
    • The modelisation approach
  • Case Studies
    • Workflows
    • Understanding your models
    • Causal inference for observational data
    • Intersectionality analysis using the MAIHDA framework
    • Interrupted Time Series Analysis
    • An Introduction to Growth Mixture Models
    • Contrasts
    • Contrasts and comparisons
    • User Defined Contrasts and Joint Tests
    • Slopes, floodlight and spotlight analysis (Johnson-Neyman intervals)
    • Contrasts and comparisons for generalized linear models
    • Contrasts and comparisons for zero-inflation models
  • News

Overview of Vignettes

Source: vignettes/overview_of_vignettes.Rmd
overview_of_vignettes.Rmd

All package vignettes are available at https://easystats.github.io/modelbased/.

Function Overview

  • Function Documentation

Introductions

Basics

  • Data grids
  • What are, why use and how to get marginal means
  • Contrast analysis
  • Marginal effects and derivatives
  • Mixed effects models

Interpretation

  • Use a model to make predictions
  • Interpret simple and complex models using the power of Effect Derivatives
  • How to use Mixed models to Estimate Individuals’ Scores

Visualization

  • Plotting estimated marginal means
  • Visualize effects and interactions
  • The Modelisation Approach to Statistics

Case Studies

Workflows

  • Understanding your models
  • Causal inference for observational data
  • Intersectionality analysis using the MAIHDA framework
  • Interrupted Time Series Analysis with modelbased
  • An Introduction to Growth Mixture Models

Contrasts

  • Contrasts and pairwise comparisons
  • User Defined Contrasts and Joint Tests
  • Slopes, floodlight and spotlight analysis (Johnson-Neyman intervals)
  • Contrasts and comparisons for generalized linear models
  • Contrasts and comparisons for zero-inflation models

Developed by Dominique Makowski, Daniel Lüdecke, Mattan S. Ben-Shachar, Indrajeet Patil, Rémi Thériault.

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