margEVT
Regularized Point Processes and Stochastic Marginalization for Extremes
Implements a non-stationary extreme value analysis framework by coupling a covariate-driven Non-Homogeneous Poisson Process (NHPP) with Elastic-Net regularization and exact analytical gradients. Provides methodologies for estimating conditional return levels and unconditional (marginalized) return levels via parametric stochastic integration over Vector Autoregressive VAR(p) covariate trajectories, or non-parametric block bootstrapping. Methodologies are based on Villa (2026) <https://sabi.ufrgs.br/> "A Novel Regularized Point Process and Stochastic Marginalization Framework for Return Level Inference under Covariate-Driven Extremes" (Master's dissertation, Universidade Federal do Rio Grande do Sul).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | margEVT_0.1.0.tar.gz |
146.3 KiB |
0.1.0 |
rolling linux/noble R-4.5 | margEVT_0.1.0.tar.gz |
146.4 KiB |
0.1.0 |
rolling source/ R- | margEVT_0.1.0.tar.gz |
44.7 KiB |
0.1.0 |
latest linux/jammy R-4.5 | margEVT_0.1.0.tar.gz |
146.3 KiB |
0.1.0 |
latest linux/noble R-4.5 | margEVT_0.1.0.tar.gz |
146.4 KiB |
0.1.0 |
latest source/ R- | margEVT_0.1.0.tar.gz |
44.7 KiB |
0.1.0 |
2026-04-23 source/ R- | margEVT_0.1.0.tar.gz |
0 B |