Assessing the performance of risk prediction models | Risk prediction models | Mathematical details on existing predictive models | Different aspects of the predictive performance | Assessing the calibration performance of a risk prediction model | A mathematical perspective | A practical perspective | How do we construct a calibration curve? | Calibration intercept and slope | Illustration of the CalibrationCurves package | Training the model | Assessing the calibration performance | ggplot version | Assessing the performance of survival models | Cox Proportional hazards model | Calibration Curve | Assessing the calibration of prediction models in clustered datasets | Clustered Group Calibration (CG-C) | Mathematical details | The equation of this model is given by$$\begin | Illustration | Two stage meta-analysis calibration (2MA-C) | Mixed model calibration (MIX-C) | Default approach | Assessing the performance of other types of prediction models | Generalized calibration curves | Illustration of the generalized calibration framework | FAQ | Why is the calibration intercept different in the rms package? | I have predicted probabilities of 0 or 1. Why is this not allowed by default and why do I get these annoying warning messages? | References

Summary of available functions | Handling multi-level factors using random effects models | Random effects model structures | Single-level random effects | Hierarchical (two-level) random effects | Just the code please | Example data sets | Buhlmann-Straub credibility model (single random effect) | Hierarchical credibility model (two-level random effects) | Combining credibility models with a GLM | Single random effect with GLM | Hierarchical random effects with GLM | Mixed models | Single random effect GLMM | Nested random effects GLMM | Balance property | References