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EQRN

Extreme Quantile Regression Neural Networks for Risk Forecasting

v0.1.2 · Nov 21, 2025 · GPL (>= 3)

Description

This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) <doi:10.1214/24-AOAS1907> (also available in preprint: Pasche and Engelke (2022) <doi:10.48550/arXiv.2208.07590>).

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r-oldrel-windows-x86_64 OK
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r-release-windows-x86_64 OK
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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies coro doFuture evd foreach future ismev magrittr torch EQRN

Version History

new 0.1.2 Mar 9, 2026