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).