Data-generating process | Scoring convergence | Running the benchmark | Results | References

Standard GLMs | Sparse and big.matrix paths | Negative-binomial regression | Firth bias-reduced GLMs | Firth across all decomposition methods | Firth on sparse and streaming designs | Hurdle models | Zero-inflated models | Summary | References

Data-generating process | Convergence comparison | Summary | Log-likelihood comparison | Theta estimates | Cases where only fastglm converges

Fitting a GLM | Decomposition methods | Stability | Inference: vcov(), robust SE, predictions | Sparse, big.matrix, and streaming designs | Native families | Negative binomial, hurdle, zero-inflation, and Firth logistic | Three R entry points | References

Logistic regression under separation | General GLM families | Binomial (logit, probit, cloglog) | Poisson (log, sqrt) | Gamma (log, inverse) | Gaussian (identity, log) | Inverse Gaussian (log) | Standard errors | Penalized deviance | Speed comparison across families | References

Negative-binomial regression | Hurdle models | Zero-inflated models | References

Example | Computational stability

Sparse design matrices | Filebacked big.matrix | Streaming from an external source | arrow / parquet recipe | When to use which path

Overview | 1. Statistical Background | 1.1 Semi-Competing Risks | 1.2 Joint Frailty Model (JFM) | 1.3 Joint Scale-Change Model (JSCM) | 1.4 Stagewise Variable Selection | 1.5 Cross-Validation | 2. Installation | 3. Data Format | 4. Simulating Data | 4.1 Joint Frailty Model data | 4.2 Joint Scale-Change Model data | 5. Joint Frailty Model (JFM) Workflow | 5.1 Fit the Stagewise Regularization Path | 5.2 Explore the Regularization Path | 5.3 Plot the Coefficient Path | 5.4 Cross-Validation | 5.5 Plot the CV Results | 5.6 Extract Coefficients and Summarize | 5.7 Baseline Hazard | 5.8 Survival Prediction | 6. Other Penalty Types (JFM) | 6.1 Lasso | 6.2 Group Lasso | 6.3 Comparing Penalties | 7. Joint Scale-Change Model (JSCM) Workflow | 7.1 Fit the Stagewise Path | 7.2 Cross-Validation | 7.3 Results | 7.4 Baseline Hazard (JSCM) | 7.5 Survival Prediction and AFT Interpretation | 8. Interpreting Output | 8.1 Alpha and Beta Conventions | 8.2 Cooperative Lasso and Variable Grouping | 8.3 Survival Curve Interpretation | 9. Default Parameters | 10. Model Evaluation | 10.1 Coefficient Recovery | 10.2 Time-Varying AUC | 11. References

Overview | $$\hat\tau_ | Basic usage | How it works with cross-fitting | Simulation comparison | User-supplied outcome predictions | When to use augmentation | References

The overfitting problem | Cross-fitting details | K-fold cross-fitting | The role of leaf size | Practical usage | Choosing the number of folds | References

Overview | Setup | Simulating data | Estimating the ATE | Forest balance | Entropy balancing (WeightIt) | Energy balancing (WeightIt) | Comparison | Covariate balance | Simulation study | Step-by-step interface

Overview | Mathematical background | The kernel energy balancing system | The kernel factorization | Direct solver (block Cholesky) | CG solver (matrix-free) | Block Jacobi preconditioned CG (default for large $n$) | Solver comparison | End-to-end timing | Scaling with number of trees | Pipeline stage breakdown | Memory usage | Summary

jcolors intro | Installation | Display all available palettes | Discrete palettes | Continuous palettes | Discrete Color Palettes | Use with ggplot2 | default | More example plots | Continuous Color Palettes | Display all continuous palettes

Introduction | Installation | Quick Start | Key Features | Available functions | Available Penalties | Available Model Families | Fitting multiple penalties at once | Timing Comparison | Linear Regression | Logistic Regression | Cross Validation | Extremely Fast Cross Validation for Linear Models | Evaluation Metrics | Misclassification Rate | Area Under the ROC Curve | Methods for Very Large Scale Problems | OEM with Precomputed $X^TX$, $X^TY$ for Linear Models | Out-of-memory Computation | Other Features | Parallelization via OpenMP | Penalty Adjustment | More Information | References

First simulate data with complicated conditional average treatment effect/benefit score | Setup | Using xgboost for estimation of ITRs | Comparing performance with linear ITRs

Introduction to personalized | Choice of $M$ function | Choice of $f$ | Variable Selection | Extension to multi-category treatments | Quick Usage Reference | Creating and Checking Propensity Score Model | Fitting Subgroup Identification Models | Evaluating Treatment Effects within Estimated Subgroups | User Guide | Overview | Creating and Checking a propensity Score Model | Observational Studies | Randomized Controlled Trials | Explanation of Major Function Arguments | x | y | trt | propensity.func | loss | method | larger.outcome.better | cutpoint | retcall | ... | Continuous Outcomes | Binary Outcomes | Count Outcomes | Time-to-event Outcomes | Efficiency Augmentation | Plotting Fitted Models | Comparing Subgroups from a Fitted Model | Validating Subgroup Identification Models | Repeated Training/Test Splitting | Bootstrap Bias Correction | Plotting Validated Models

Efficiency augmentation | Propensity score utilities | Augmentation utilities | Comparing performance with augmentation

Example with multi-category treatments | More details on propensity scores for multi-category treatments