nsevt — non-stationary extreme-value tail inference

nsevt is a dependency-light Python package for five connected tasks:

1. peaks-over-threshold generalized Pareto (GPD) estimation with an adaptive profile-likelihood interval for the shape and a bootstrap of the finite endpoint;
2. an interval-censored (grouped) GPD fit for discretised data, with profile-likelihood intervals for the shape and the endpoint, which removes the bias that rounding induces in both;
3. a likelihood-ratio trend test calibrated by complete-block label permutation, with a grid-independent minimum-detectable effect;
4. Monte Carlo power and signed minimum-detectable-effect (MDE) analysis; and
5. a pre-specified multi-source robustness analysis that distinguishes non-reproduction with adequate power from an unresolved comparison.

The package uses deliberately measured terminology. A negative GPD shape point estimate gives a finite model-conditional statistical endpoint. nsevt reports support for a negative shape only when the entire 95% profile interval lies below zero; neither result is called a physical ceiling.

Install

From PyPI:

pip install nsevt

For development or the optional Streamlit demonstration:

git clone https://github.com/GaiskaSalomon/nsevt.git
cd nsevt
pip install -e ".[dev,demo]"

Quick start

import numpy as np
import nsevt

rng = np.random.default_rng(7)
threshold, xi, sigma = 40.0, -0.25, 10.0
years = np.repeat(np.arange(1980, 2030), 12)
uniform = rng.uniform(size=years.size)
excess = sigma / xi * ((1 - uniform) ** (-xi) - 1)
values = threshold + excess

fit = nsevt.gpd_pot(values, threshold=threshold, n_boot=300)
print(fit.summary())
print("negative-shape estimate:", fit.bounded_estimate)
print("negative shape supported by 95% CI:", fit.bounded_supported)

trend = nsevt.trend_permutation(excess, years, n_perm=999)
print("LR permutation p:", trend["p_permutation"],
      "+/-", trend["p_permutation_mcse"])

mde = nsevt.min_detectable_effect(
    excess, years, direction="both", n_rep=200, n_perm_calibration=499
)
print("positive MDE:", mde["mde_positive"])
print("negative MDE:", mde["mde_negative"])
print("interpolated EMD:", mde["emd_positive"], mde["emd_positive_ci95"])

Discretised (grouped) data

When values are recorded on a grid (for example wind speeds in 5 kt steps), fitting the continuous GPD to the rounded values biases the shape and the finite endpoint. gpd_pot_grouped fits the interval-censored likelihood instead and reports a profile-likelihood interval for both the shape and the endpoint (the endpoint interval profiles the reparameterised endpoint, not a percentile bootstrap).

fit = nsevt.gpd_pot_grouped(values, threshold=40, grid=5.0)
print(fit.summary())
print("shape 95% CI:", fit.xi_ci95)
print("endpoint 95% CI:", fit.endpoint_ci95)

Multi-source robustness

Sources must be genuinely distinct products with comparable temporal support; early and late halves of one record are not substitutes.

result = nsevt.multisource_robustness(
    [("product A", values_a, years_a),
     ("product B", values_b, years_b)],
    threshold=40,
    reference="product A",
)
print(result.trend_status)
print(result.table())

Possible trend statuses include reproduced, inconsistent_direction, not_reproduced_with_power, not_resolved, and no_reference_signal. Agreement or disagreement across sources is a robustness result; it does not by itself attribute a discrepancy to instruments, homogenization, or physical change.

Stable and experimental functionality

The exact assumptions and claim boundaries are documented in docs/assumptions.md. Experimental APIs are retained for evaluation but are not part of the package's central inferential claim.

Reproducibility and tests

The implementation was adapted from research pipelines and then regression- checked; it is not represented as a verbatim copy. Randomized routines accept a seed and report the number of successful replicates. Run the validation suite with:

pip install -e ".[dev]"
ruff check src tests demo
pytest --cov=nsevt --cov-report=term-missing --cov-fail-under=80
python -m build
python -m twine check dist/*

See docs/validation.md for what each test establishes and, equally importantly, what it does not establish.

Citation and license

Use CITATION.cff or the Zenodo DOI shown above. nsevt is released under the MIT License; see LICENSE.