margEVT

• summary() returns an object of class summary.nhpp_fit (printed as before) instead of printing directly.
• The vars dependency was removed. VAR(p) generators are fitted by least squares with Schwarz order selection on a common sample, which reproduces vars::VARselect() and vars::VAR(); a single covariate is a VAR(p) with K = 1 rather than a separate AR model.
• Random-number streams changed: bootstrap replicates use independent L'Ecuyer-CMRG streams and trajectories are simulated jointly, so results for a given seed differ from 0.3.0.

Estimation

• Adaptive LASSO: penalty_weights = "adaptive" computes weights 1 / |theta|^adaptive_gamma from an unpenalized fit (ridge fallback); numeric coordinate weights are also accepted.
• New fit_nhpp() arguments lambda_grid, init, and grad_tol. The optimizer falls back from BFGS to L-BFGS-B and a restarted BFGS when the KKT check fails.
• Fitted objects store the penalty weights, the KKT violation, the evaluated regularization path (fit$path), and the observed series.
• The likelihood, gradient, and penalty are evaluated once per parameter vector and shared between objective and gradient calls.

Methods and diagnostics

• New methods logLik(), nobs(), predict(), residuals(), and coef() for nhpp_fit; AIC() and BIC() work through logLik().
• residuals() returns time-rescaling interarrival residuals and magnitude residuals, both standard exponential under the model.
• plot.nhpp_fit() gains type = "path" (BIC curve and coefficient paths) and type = "qq" (exponential Q-Q plots with exact pointwise bands).
• n_exceedances() uses the stored series when y is omitted.

Marginalization and covariates

• New seasonal_harmonics(); harmonics of any order (cos<k>, sin<k>, sen<k>) are recognized and rebuilt automatically.
• marginalize() returns an nhpp_rl object with Monte Carlo standard errors of the return-level roots (se_mc), accepts fixed covariate values (e.g. list(trend = 1)), fills inactive design columns silently, and has a plot() method.
• fit_var_generator() gains n_harmonics, excludes deterministic variables from the VAR, and supports derived covariates (lags, moving averages) through derive and derive_lead. simulate_covariates() simulates all trajectories at once and gains seed.

Bootstrap and walk-forward assessment

• Bootstrap refits start from the original estimate and keep the original penalty weights, gradient tolerance, and scaling. Results do not depend on the new cores argument; replicate matrices are returned as the attribute "replicates".
• backtest() gains trend_col (trend renormalized within each training window), fixed, reselect_lambda, and cores. Adaptive weights are recomputed inside each training window, and covariates inactive in a window refit are filled without warnings.

Documentation

• New vignette, examples for every exported function, and an updated citation file.