Two-Way Within-Subjects ANOVA | Basic Plot | Exploring Graphical Options and Themes | Saving Plots and Plot Sizes | Data in the Background | Single Geom | Multiple Geoms | Adding Color to Plots | Plotting Order and Error Bars | One-way Plots Without Trace Factor | 3-Way Mixed Model | Data and Model | Which Data to Plot in the Background | Ways of Plotting Data in the Background | Error Bars for Mixed Models | References

Introduction | Base R stats models: lm, glm | nlme mixed model | glmmTMB | GLMMadaptive | rstanarm | brms

Overview | Description of Experiment and Data | Data and R Preparation | Descriptive Analysis | Model Setup | Finding the Final Model | Results of Maximal and Final Model | LRT Results | Summary of Results | Follow-Up Analyses | task:stimulus:frequency Interaction | task:stimulus:density:frequency Interaction | References

Foreword by Henrik Singmann | Testing the Empirically Testable Assumptions | Homogeneity of Variances | What to do when assumption is violated? | Sphericity | Normalicy of Residuals | References

Introduction | Algorithmic complexity for short strings | The acss packages | Applications | Relationship to complexity based model selection | Conclusion

Overview | Data and Research Question | ANOVA Analysis | Mixed Model Analysis | Setting-Up and Fitting the Models | Dealing with Fit Warnings | ANOVA Table for GLMMs | Follow-Up Analysis and Plots | Analysis of Two Studies with GLMM | Estimating Models in Parallel | Type III versus Type II Sums of Squares | Follow-Up Tests for Type II Models | References

Overview | Description of Experiment and Data | Data and R Preperation | ANOVA | Post-Hoc Contrasts and Plotting | Some First Contrasts | Main Effects Only | A Simple interaction | Running Custom Contrasts | Plotting | Basic Plots | Customizing Plots | Replicate Analysis from Singmann and Klauer (2011) | Final Note | References

Introduction | Benchmarking the Density Function Approximations | Generating Benchmark Data | Analysis of Benchmark Results | Benchmarking Model Fitting to Real-World Data | Generating Benchmark Data for Parameter Estimation | Log-Likelihood Functions | Fitting Function | Running the Fitting | Analysis of Benchmark Results | References

Mathematical Background | The Distribution Function Approximations | Infinite Sum Methods | Normal CDF | Mills Ratio | PDE Method | Benchmarking the Distribution Function Approximations | Vectorized Benchmark Data and Results | Non-vectorized Benchmark Data and Results | References

Introduction | Fitting with ddm() | Simple Fitting Routine | Rudimentary Analysis | Fitting Manually with dfddm() | Log-likelihood Function | Simple Fitting Routine | Fitting the Entire Dataset | Rudimentary Analysis | References

Mathematical Background | The Density Function Approximations | Large-Time | Small-Time | Navarro & Fuss | Gondan, Blurton, Kesselmeier | Stop When Small Enough (SWSE) | Combining Large-Time and Small-Time | Navarro Small & Navarro Large | Gondan Small & Navarro Large | Stop When Small Enough (SWSE) Small & Navarro Large | First Heuristic: Large-Time Efficiency | Second Heuristic: Effective Response Time | References

Background | Validating the Density Function Approximations | Generating Data | Testing the Density Function Approximations | Known Errors (KE) | Validating Fitting (Optimization) Using the Density Function Approximations | Generating the Parameter Estimates Using Real-World Data | Log-Likelihood Functions | Fitting Function | Running the Fitting | Testing the Fitted Parameters | References

Description of the Experiment | Descriptive data | Diffusion Model Analysis | Graphical Model Fit | Predicted Response Rates | Predicted Median RTs | All quantiles | LBA analysis | Comparing Model Fit | References

Introduction | An example MPT: The Two-High-Threshold Model of Recognition Memory | Representation, Estimation and Inference in MPT Models: A Brief Overview | General Overview of MPTinR | Appendix: MPTinR Algorithms

Fixed Effects, Random Effects, and Non-Independence | Setting up a Mixed Model | Fitting Mixed Models in R | Beyond Linear Mixed Models and the Identity Link Function | Summary

Bayes' Theorem | Model-Based Bayesian Inference | Components of Bayesian Inference | Prior Distributions | Hierarchical Bayes | Conjugacy | Likelihood | Numerical Approximation | Prediction | Bayes Factors | Model Fit | Posterior Predictive Checks | Advantages Of Bayesian Inference Over Frequentist Inference | Advantages Of Frequentist Inference Over Bayesian Inference

Adaptive Logistic Basis (ALB) Regression | ANCOVA | ANOVA, One-Way | ANOVA, Two-Way | Approximate Bayesian Computation (ABC) | AR(p) | AR(p)-ARCH(q) | AR(p)-ARCH(q)-M | AR(p)-GARCH(1,1) | AR(p)-GARCH(1,1)-M | AR(p)-TARCH(q) | AR(p)-TARCH(q)-M | Autoregressive Moving Average, ARMA(p,q) | Beta Regression | Beta-Binomial | Binary Logit | Binary Log-Log Link Mixture | Binary Probit | Binary Robit | Binomial Logit | Binomial Probit | Binomial Robit | Change Point Regression | Cluster Analysis, Confirmatory (CCA) | Cluster Analysis, Exploratory (ECA) | Conditional Autoregression (CAR), Poisson | Conditional Predictive Ordinate | Contingency Table | Discrete Choice, Conditional Logit | Discrete Choice, Mixed Logit | Discrete Choice, Multinomial Probit | Distributed Lag, Koyck | Dynamic Sparse Factor Model (DSFM) | Exponential Smoothing | Factor Analysis, Approximate Dynamic | Factor Analysis, Confirmatory | Factor Analysis, Exploratory | Factor Analysis, Exploratory Ordinal | Factor Regression | Gamma Regression | Geographically Weighted Regression | Hidden Markov Model | Inverse Gaussian Regression | Kriging | Kriging, Predictive Process | Laplace Regression | Latent Dirichlet Allocation | Linear Regression | Linear Regression, Frequentist | Linear Regression, Hierarchical Bayesian | Linear Regression, Multilevel | Linear Regression with Full Missingness | Linear Regression with Missing Response | Linear Regression with Missing Response via ABB | Linear Regression with Power Priors | Linear Regression with Zellner's g-Prior | LSTAR | MANCOVA | MANOVA | Mixed Logit | Mixture Model, Finite | Mixture Model, Infinite | Multinomial Logit | Multinomial Logit, Nested | Multinomial Probit | Multiple Discrete-Continuous Choice | Multivariate Binary Probit | Multivariate Laplace Regression | Multivariate Poisson Regression | Multivariate Regression | Negative Binomial Regression | Normal, Multilevel | Ordinal Logit | Ordinal Probit | Panel, Autoregressive Poisson | Penalized Spline Regression | Poisson Regression | Polynomial Regression | Proportional Hazards Regression, Weibull | PVAR(p) | Quantile Regression | Revision, Normal | Ridge Regression | Robust Regression | Seemingly Unrelated Regression (SUR) | Simultaneous Equations | Space-Time, Dynamic | Space-Time, Nonseparable | Space-Time, Separable | Spatial Autoregression (SAR) | STARMA(p,q) | State Space Model (SSM), Linear Regression | State Space Model (SSM), Local Level | State Space Model (SSM), Local Linear Trend | State Space Model (SSM), Stochastic Volatility (SV) | Threshold Autoregression (TAR) | Time Varying AR(1) with Chebyshev Series | Variable Selection, BAL | Variable Selection, Horseshoe | Variable Selection, LASSO | Variable Selection, RJ | Variable Selection, SSVS | VARMA(p,q) - SSVS | VAR(p)-GARCH(1,1)-M | VAR(p) - Minnesota Prior | VAR(p) - SSVS | Weighted Regression | Zero-Inflated Poisson (ZIP)

Installation | Data | Specifying a Model | Initial Values | Numerical Approximation | Summarizing Output | Plotting Output | Posterior Predictive Checks | General Suggestions | Independence and Observability | High Performance Computing | Details | Bayesian-Inference.com | Conclusion