margEVT 0.4.0
Breaking changes
fit_nhpp(lambda_scaling = ) now defaults to
"common" (one scalar penalty for all parameter blocks), the
convention used in the dissertation and articles. Use
lambda_scaling = "gradient" for the previous
score-calibrated block penalties.- With
lambda = "bic", the returned model is now the
regularization-path solution at the selected penalty, so the reported
fit is exactly the one scored by the BIC.
- Convergence is now certified by the Karush-Kuhn-Tucker conditions of
the penalized problem (
grad_tol, scaled by the number of
exceedances) rather than by the optimizer’s return code. Fits that
previously reported convergence may now warn, and vice versa.
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.
margEVT 0.3.0
Estimation
consistency and reproducibility
- Added
lambda_scaling to fit_nhpp().
Automatic BIC selection can retain the score-calibrated block penalties
introduced in 0.2.0 or use a common scalar penalty to reproduce the
estimation convention used in the dissertation analyses.
- Corrected automatic BIC selection so that both the unpenalized
likelihood and the effective parameter count are evaluated at the
thresholded operational estimator. Smooth optimizer solutions, rather
than thresholded vectors, are retained as warm starts along the
regularization path.
- Fitted objects now store both the smooth optimizer solution
(
par) and the operational activity-thresholded solution
(par_oper). Downstream parameter prediction uses the
operational estimator by default, while
coef(fit, operational = FALSE) preserves access to the
smooth solution.
- Coefficient bootstrap replicates now use the operational estimator,
allowing percentile distributions to retain an atom at zero.
- Univariate covariate generators now fit a genuine stable AR model
selected by BIC instead of padding the series with an artificial noise
coordinate.
- Covariate simulation now supports exact Gaussian stationary-state
initialization, while retaining zero-state burn-in as an explicit
sensitivity option.
Validation and defensive
checks
- Return-level integration now rejects non-finite fitted parameters
and non-positive scales instead of silently omitting invalid
contributions.
- Parametric and empirical marginalization verify all direct and
interaction input covariates before constructing annual
trajectories.
- Prediction matrices are rebuilt directly from the stored design
columns, avoiding formula reinterpretation of non-syntactic or
interaction-like column names.
- Package metadata and the dissertation citation were synchronized for
the 0.3.0 development line.
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.
Inference and documentation
- 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.
margEVT 0.1.1
- Added
plot.nhpp_fit() for cumulative-intensity and
fitted-parameter plots.
- Added package URL and bug-report metadata.