API Reference
Simulators
| Function | Description |
|---|---|
generate_binary_dataset |
Synthetic binary-label dataset |
generate_stratified_binary_dataset |
Stratified binary-label dataset |
generate_binary_dataset_with_oracle_sampling |
Binary dataset with oracle sampling probabilities |
generate_gaussian_dataset |
Synthetic Gaussian dataset |
generate_clustered_binary_dataset |
Synthetic clustered binary-label dataset |
generate_multi_binary_dataset |
Synthetic binary-label dataset with multiple proxies |
simulate_annotation |
Simulate annotation in the simulation lifecycle |
Samplers
| Class | Description |
|---|---|
UniformSampler |
Uniform random sampling |
ActiveSampler |
Uncertainty-based active sampling |
StratifiedSampler |
Stratified budget allocation with Neyman/proportional strategies |
CostOptimalRandomSampler |
Cost-optimal random sampling |
CostOptimalSampler |
Uncertainty-based cost-optimal sampling |
UniformClusteredSampler |
Uniform clustered random sampling |
Estimators
Classical
| Class | Description |
|---|---|
ClassicalMeanEstimator |
Classical sample mean without proxy labels |
StratifiedClassicalMeanEstimator |
Classical mean with population-proportional stratification |
IPWClassicalMeanEstimator |
Classical mean with inverse probability weighting |
ClusteredClassicalMeanEstimator |
Classical sample mean on clustered data |
Prediction-Powered
| Class | Description |
|---|---|
PPIMeanEstimator |
Combines labeled data with proxy predictions |
StratifiedPPIMeanEstimator |
PPI with per-stratum optimal weighting |
ClusteredPPIMeanEstimator |
PPI for clustered data |
ASIMeanEstimator |
Active statistical inference with non-uniform sampling |
MultiPPIMeanEstimator |
Combines labeled data with predictions from multiple proxies |
PTDMeanEstimator |
Predict-then-debias with bootstrap confidence intervals |
StratifiedPTDMeanEstimator |
PTD with per-stratum optimal weighting |
ClusteredPTDMeanEstimator |
PTD for clustered data |
IPWPTDMeanEstimator |
PTD with inverse probability weighting |
Monitors
Classical
| Class | Description |
|---|---|
EmpiricalClassicalMeanMonitor |
Anytime-valid drift monitor over batched labels, without proxy predictions |
AsymptoticClassicalMeanMonitor |
Anytime-valid drift monitor over batched labels via an asymptotic confidence sequence, without proxy predictions |
Prediction-Powered
| Class | Description |
|---|---|
EmpiricalPPIMeanMonitor |
Anytime-valid drift monitor combining true and proxy labels |
Confidence Intervals
| Class | Description |
|---|---|
CLTConfidenceInterval |
CLT-based normal approximation confidence intervals |
BootstrapConfidenceInterval |
Quantile-based bootstrap confidence intervals |
Confidence Sequences
| Class | Description |
|---|---|
EmpiricalBernsteinConfidenceSequence |
Anytime-valid empirical-Bernstein confidence sequence |
AsymptoticConfidenceSequence |
Anytime-valid asymptotic confidence sequence |
Inference Results
| Class | Description |
|---|---|
ClassicalMeanInferenceResult |
Result object from classical estimators |
PredictionPoweredMeanInferenceResult |
Result object from prediction-powered estimators |
Monitoring Results
| Class | Description |
|---|---|
ClassicalMeanMonitoringResult |
Result object from classical monitors |
PredictionPoweredMeanMonitoringResult |
Result object from prediction-powered monitors |
Scientific Validation
| Function | Description |
|---|---|
run_monte_carlo |
Monte Carlo driver for coverage and efficiency validation |
compute_hits |
Per-seed hit indicators for coverage computation |
coverage_with_error_bar |
Empirical coverage and confidence interval |
I/O
| Module | Description |
|---|---|
glide.io |
JSON serialization and export helpers |