margEVT 0.2.0

Consistency and temporal resolution

Added active_tol to fit_nhpp() and stored it in fitted objects. The same tolerance is now used by automatic BIC selection, print(), summary(), bic_nhpp(), active_covariates(), bootstrap refits, and downstream covariate generators.
build_cov_annual(), fit_var_generator(), simulate_covariates(), marginalize(), and backtest() now inherit the temporal resolution from the fitted model unless the user supplies an explicit override.
Added frequency-aware behavior for weekly and other non-daily analyses.
fit_var_generator() now verifies the VAR root condition, stores the root moduli and spectral radius, and refuses to simulate from an unstable fit.
simulate_covariates() now generates independent annual Monte Carlo paths, each with its own burn-in, instead of splitting one dependent long path into nominal annual replicates.
Corrected GEV endpoint handling in marginalized probabilities: levels below the lower endpoint when the shape is positive now have zero non-exceedance probability, while levels above a finite upper endpoint retain probability one conditionally on that trajectory.

Backtesting now reports not_rejected alongside the legacy calibrated field and uses language appropriate for nominal binomial comparisons and exploratory tail-conditional PIT diagnostics.
Validation years with insufficient observed coverage are now excluded from binomial denominators instead of being silently counted as non-exceedances.
Approach C now uses a locally preserved random seed, preventing its annual block sampling from resetting the outer bootstrap stream and repeating subsequent parametric bootstrap draws.
bootstrap_rl() now reports the original fitted-model return level in RL_est; previous versions reported the mean of successful bootstrap roots.
Clarified that the bootstrap is conditional on the observed occurrence pattern and selected model specification.
Corrected the documentation of empirical annual-block marginalization and the event-based BIC convention.
Added strict validation for block-specific penalty controls, return periods, Monte Carlo samples, and covariate trajectory lengths to prevent silent recycling or malformed fits.
Return-level root finding no longer expands below the fitted threshold. It expands only the upper bracket and returns NA when the requested quantile lies outside the point-process tail domain.

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