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).

Version:	0.1.0
Imports:	stats, utils, vars
Suggests:	dplyr, tidyr, extRemes, testthat (≥ 3.0.0)
Published:	2026-07-23
DOI:	10.32614/CRAN.package.margEVT (may not be active yet)
Author:	Rodrigo Fonseca Villa [aut, cre], Flavio Ziegelmann [ths]
Maintainer:	Rodrigo Fonseca Villa <rodrigo03.villa at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
Citation:	margEVT citation info
Materials:	README
CRAN checks:	margEVT results

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