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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).
Documentation:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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[aut, cre],
Flavio Ziegelmann