1. Overview | 2. Data | 3. Train / test split | 4. Preprocessing | 5. Fit with fbrglm | 6. Prediction | 7. Evaluation | 8. Visualization | 9. Inspecting and reusing the fit | 10. What fbrglm saved us from | 11. Reproducibility

What fbrglm is for | A small binomial example | Choosing lambda | Factor predictors | Missing values | Offsets | Reaching the underlying glmnet objects | Scope of the current release

1. The same model in glm | 2. The same model in raw glmnet | 3. The same model in fbrglm | 4. Side-by-side comparison | 5. Pipe workflow | 6. Safe prediction under narrowed factor levels | 7. Choosing when to use which interface

Overview | Summary of supported model types | Linear regression | Logistic regression | Poisson regression with an offset | Piecewise exponential survival model via Poisson regression | Native Cox regression | Gamma regression | Negative binomial regression (fixed θ) | Multinomial regression (experimental) | Multi-response Gaussian (experimental) | Limitations