Dependence-aware block-maxima inference

Severity, persistence, and design-life levels under serial dependence.

UniBM is a Python package for dependence-aware block-maxima inference in environmental extremes. It keeps severity inference, persistence inference, and design-life levels in one coherent workflow while exposing a small public API under unibm, unibm.evi, unibm.ei, and unibm.cdf.

Start with the package See runnable examples

Guide

Getting Started

Install the package, sync the local environment, and make the first severity or persistence call.

Concepts

Read the conceptual split between the severity branch, the persistence branch, and design-life levels.

Worked Examples

Use short, runnable examples for the public package surface without stepping into the full repo orchestration.

Reading Returned Objects

Interpret the most useful result fields and understand what the fitted objects actually contain.

API Surface

Public API

Start from the top-level package namespace and the recommended entrypoints.

EVI Namespace

Severity-side estimation, block-quantile scaling, and design-life helpers.

EI Namespace

Persistence-side procedures for extremal-index estimation under dependence.

CDF Helper

Supporting helper functionality exposed as part of the public package surface.

This site is intentionally package-focused. Repository-level orchestration for benchmarks, applications, and notebook rebuilds remains in the root README and justfile.