quantile-guard

The quantile modeling toolkit with non-crossing guarantees.

Fit multiple quantiles jointly with monotonicity constraints that guarantee predictions never cross. Built-in inference, conformal calibration, evaluation metrics, and crossing diagnostics. Scikit-learn compatible.

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Why Not Just Fit Quantiles Independently?

When you fit quantiles one at a time (as sklearn and statsmodels do), nothing prevents the 90th percentile prediction from falling below the 10th. On real data with heavy tails or many quantile levels, this happens frequently:

n	features	quantiles	Crossing rate (independent)	Crossing rate (quantile-guard)
500	10	13	30.0%	0%
1,000	10	13	16.5%	0%
2,000	20	13	11.0%	0%

quantile-guard eliminates crossings by construction — and in these benchmarks the joint formulation often acts as beneficial regularization, with comparable or better pinball loss in most settings.

Full benchmark results | Reproduce locally

Who Is This For

• Data scientists building prediction intervals for production systems where crossed quantiles break downstream logic
• Researchers & econometricians who need valid statistical inference (SEs, p-values, CIs) on quantile regression coefficients
• ML engineers who want a drop-in sklearn-compatible estimator with monotone quantile guarantees
• Risk analysts & actuaries modeling conditional tail distributions with censored or survival data
• Anyone evaluating quantile models — the metrics and diagnostics modules work with predictions from XGBoost, LightGBM, or any other source

Workflows

This is a toolkit, not a single estimator. It covers the pipeline from raw quantile regression through calibrated prediction intervals:

Workflow	What it does
Joint Quantile Regression	Fit multiple quantiles in one call with non-crossing guarantees
Conformalized Quantile Regression	Calibrate intervals for finite-sample coverage guarantees
Censored Quantile Regression	Handle right- or left-censored (survival) data
Evaluation & Metrics	Pinball loss, coverage, interval score, crossing diagnostics
Calibration Diagnostics	Coverage by group/bin, nominal vs empirical, sharpness analysis
Crossing Detection & Repair	Diagnose and fix crossings from any quantile model

Feature comparison vs sklearn & statsmodels

Feature	quantile-guard	sklearn	statsmodels
Multiple quantiles (joint fit)	Yes	No	No
Non-crossing guarantee	Yes	No	No
Multi-output regression	Yes	No	No
Analytical / kernel / cluster / bootstrap SEs	Yes	No	Partial
L1 / Elastic Net / SCAD / MCP	Yes	L1 only	No
Conformal calibration (CQR)	Yes	No	No
Calibration diagnostics	Yes	No	No
Evaluation metrics suite	Yes	Partial	No
Crossing detection + fix	Yes	No	No
Censored QR	Yes	No	No
Prediction intervals	Yes	No	No
Pseudo R-squared	Yes	No	Yes
Formula interface	Yes	No	Yes
Sklearn pipeline compatible	Yes	Yes	No

Installation

pip install quantile-guard

Optional extras:

pip install quantile-guard[all]      # formula interface + plots
pip install quantile-guard[plot]     # matplotlib only
pip install quantile-guard[formula]  # patsy only

Migrating from quantile-regression-pdlp? For the core estimator, the import path changes but the call pattern stays the same:

# before
from quantile_regression_pdlp import QuantileRegression
# after
from quantile_guard import QuantileRegression

Quick Start

import numpy as np
from quantile_guard import QuantileRegression

X = np.random.default_rng(0).normal(size=(200, 3))
y = X @ [2.0, -1.5, 0.8] + np.random.default_rng(1).normal(scale=0.5, size=200)

# Fit 3 quantiles jointly — guaranteed non-crossing
model = QuantileRegression(tau=[0.1, 0.5, 0.9], se_method='analytical')
model.fit(X, y)

# Summaries with coefficients, SEs, p-values, and 95% CIs
print(model.summary()[0.5]['y'])

# Prediction intervals (guaranteed monotone: lower <= median <= upper)
interval = model.predict_interval(X[:5], coverage=0.80)
print(interval['y']['lower'], interval['y']['upper'])

Conformal Calibration

Turn raw quantile predictions into intervals with coverage guarantees:

from quantile_guard.conformal import ConformalQuantileRegression

base = QuantileRegression(tau=[0.05, 0.5, 0.95], se_method='analytical')
cqr = ConformalQuantileRegression(base_estimator=base, coverage=0.90)
cqr.fit(X_train, y_train)

intervals = cqr.predict_interval(X_test)
print(cqr.empirical_coverage(X_test, y_test))  # typically close to the target coverage

Censored Quantile Regression

For survival data with right- or left-censoring:

from quantile_guard import CensoredQuantileRegression

model = CensoredQuantileRegression(tau=0.5, censoring='right', se_method='analytical')
model.fit(X, observed_time, event_indicator=delta)

Evaluate Any Quantile Model

The metrics and diagnostics modules work with predictions from any source — not just this package:

from quantile_guard.metrics import quantile_evaluation_report
from quantile_guard.postprocess import crossing_summary

# Evaluate predictions from XGBoost, LightGBM, or any other model
report = quantile_evaluation_report(y_true, predictions, taus)
crossings = crossing_summary(predictions, taus)

Regularization

QuantileRegression(tau=0.5, regularization='l1', alpha=0.1)       # Lasso
QuantileRegression(tau=0.5, regularization='elasticnet', alpha=0.1, l1_ratio=0.5)
QuantileRegression(tau=0.5, regularization='scad', alpha=0.3)     # Less bias on large coefficients
QuantileRegression(tau=0.5, regularization='mcp', alpha=0.3)

Inference Options

QuantileRegression(tau=0.5, se_method='analytical')   # Fast asymptotic SEs
QuantileRegression(tau=0.5, se_method='kernel')        # Heteroscedasticity-robust
QuantileRegression(tau=0.5, se_method='bootstrap', n_bootstrap=500)
# Cluster-robust SEs
model.fit(X, y, clusters=group_labels)

Benchmarks

Tested on heavy-tailed heteroscedastic data (Student-t noise, 10-20 features, up to 13 quantiles). Independent fitters cross; quantile-guard does not — while matching prediction quality and improving it in the hardest small-sample settings:

n	features	quantiles	Crossing (quantile-guard)	Crossing (sklearn)	Pinball (quantile-guard)	Pinball (sklearn)
500	10	7	0%	11.0%	0.5148	0.5166
500	10	13	0%	30.0%	0.5095	0.5240
1,000	10	13	0%	16.5%	0.5048	0.5071
2,000	20	13	0%	11.0%	0.5599	0.5611

Speed tradeoff: quantile-guard solves a single joint LP with non-crossing constraints, which is slower than fitting each quantile independently. The value is the guarantee and the richer downstream workflows. For single-quantile fits where speed matters most, sklearn or statsmodels may be more appropriate.

Full benchmark results | Reproduce locally

When to Use This vs Alternatives

Use quantile-guard when you need:

• Multiple quantile predictions that must not cross (production pipelines, interval forecasts)
• Statistical inference on quantile coefficients (SEs, p-values, confidence intervals)
• Calibrated prediction intervals (conformal quantile regression)
• Censored/survival quantile models
• A complete evaluation workflow for any quantile model's predictions

Use sklearn or statsmodels when:

• You only need a single quantile (e.g., median regression)
• Raw speed matters more than crossing guarantees
• You don't need inference, calibration, or evaluation tooling

Documentation

Full docs: joshvern.github.io/quantile_guard

Under the Hood

Quantile regression is naturally a linear program. quantile-guard solves joint multi-quantile LPs with non-crossing constraints using:

• PDLP — first-order primal-dual solver (default, from Google OR-Tools)
• GLOP — revised simplex (faster on small/medium problems)
• HiGHS — via scipy's sparse LP interface (memory-efficient)

QuantileRegression(tau=0.5, solver_backend='GLOP')   # simplex
QuantileRegression(tau=0.5, use_sparse=True)          # scipy sparse

Dependencies

Required: numpy, pandas, scipy, scikit-learn, ortools, tqdm, joblib

Optional: matplotlib (plots), patsy (formulas), statsmodels (benchmarks)

Contributing

Contributions welcome. Open an issue or pull request on GitHub.

License

MIT