Extreme Value Analysis (EVA) in Python

scikit-extremes is a basic statistical package to perform univariate extreme value calculations using Python

Modelling extreme values

Partially-Interpretable Neural Networks for Extreme Value modelling

The repo contains the main topics carried out in my master's thesis on operational risk. In particular, it is described how to implement the so called Loss Distribution Approach (LDA), which is considered the state-of-the-art method to compute capital charge among large banks.

Threshold Selection and Uncertainty for Extreme Value Analysis

Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

CRAN Task View: Extreme Value Analysis

R package to apply the transformed-stationary extreme value analysis

Loglikelihood Adjustment for Extreme Value Models

Repository for the paper: "Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow".

Likelihood-Based Inference for Time Series Extremes

Generalised Additive Extreme Value Models for Location, Scale and Shape

R-functions for the metastatistical extreme value distribution

Extreme value analysis using MATLAB

Official implementation of "Extreme Value Meta-Learning for Few-Shot Open-Set Recognition of Hyperspectral Images" (TGRS'23)

This repository contains code, data, output, and figures associated with the A univariate extreme value analysis and change point detection of monthly discharge in Kali Kupang, Central Java, Indonesia manuscript

Extreme value statistics

This repository includes some applications of extreme value analysis techniques for modeling wildfire data. It's a work in progress :) — feel free to reach out if you'd like more details!