Introduction | Illustrations | Session info

Introduction | Prostate cancer data analysis | S3 Methods

Introduction | Continuous outcome: Immunotoxicity study on Chloramine | Binary outcome: Efficacy study of new chemotherapy | Survival outcome: Longevity study of male fruit flies | Meta-analysis: Efficacy study of Enoxaparin | Conclusion

Introduction | Normal and Non-normal Mixed-effects Models | Models for Binary Outcomes | Proportional Odds Models for Bounded Responses | Marginally Interpretable Weibull and Cox Models | Simulations | Change Log

Introduction | Normal Linear Regression Models | Box-Cox Models | Continuous Outcome Logistic Regression | Connection to Quantile Regression | Survival Analysis | Ordinal Regression | Transformation Trees and Forests | Summary

Introduction | Discrete Hazards Cox Count Transformation Model | Logistic Count Transformation Model | Discrete Reverse Time Hazards Count Transformation Model | Probit Count Transformation Model | Summary

Introduction | Applications | Artificial Data Generating Processes

Introduction | Specifying Transformation Models | Most Likely Transformations | Transformation Analysis | Classical Likelihood Inference | Simulation-based Likelihood Inference | Summary | The variables Package | The basefun Package | The mlt Package

Introduction | Application | Independent observations | Dependent censoring | Dependent observations | Personalised medicine | Other extensions | Discussion | Comparative overview of R implementations