plot_aggregate_contributions() labelled flagged
observations with a fixed offset above each point, so labels for
observations close together on the index axis overlapped and became
unreadable. Labels are now staggered vertically for points that fall
within a closeness threshold of each other, leaving isolated points
unaffected.envelope_qq() saved and restored the caller’s
entire par() state
(par(no.readonly = TRUE)), which includes the multi-panel
layout position (mfg). When called as one step of a
multi-panel figure set up by the caller
(par(mfrow = c(1, 2)) followed by
envelope_qq() then a second plot()), restoring
the saved state on exit rewound the layout position, so the second plot
overwrote the first panel instead of advancing to the second, leaving
one panel blank. The function now saves and restores only
pty, the one parameter it actually changes.evbs_monitor() computes the prospective
endpoint-identifiability index \(F_t\),
combining the conformal normal curvature with the finite upper endpoint,
and plot.evbs_monitor() draws the corresponding control
chart. Values below one indicate that the endpoint is determined by a
single observation and should not be quoted as a design value.logEVBS() provides a gamlss.family
implementation of the log-EVBS distribution, allowing every parameter,
including the tail-shape parameter, to depend on covariates or smooth
terms. With a constant predictor it reproduces the fixed-parameter fit
of evbsreg.fit(). Companion functions
dlogEVBS(), plogEVBS() and
qlogEVBS() are also provided.devbs(), pevbs() and qevbs()
complete the distribution family. The package previously exported only
revbs(), so users had no way to compute quantiles or return
levels.evbs_endpoint() returns the finite upper endpoint of
the fitted model when gama < 0 (Weibull max-domain of
attraction).evbs_return_level() computes return levels and expected
shortfall from the exact EVBS quantile function.gevreg.fit(), gev_scores() and
cnc_diagnostics_gev() extend the local influence framework
to the generalized extreme-value regression model.evbs_block_boot() provides moving-block bootstrap
standard errors for series with residual serial dependence.2 * gama, not gama. The transformation
preserves the max-domain of attraction but doubles the tail index.
Documentation updated accordingly.qevbs() or
evbs_return_level().gevreg.fit() centres are strongly recommended: the GEV
likelihood is poorly conditioned for uncentred covariates and may
silently fail to converge.Initial CRAN Release - Complete implementation of local influence diagnostics for Extreme-Value Birnbaum-Saunders (EVBS) regression models
Estimation - evbsreg.fit() function
for joint maximum likelihood estimation of EVBS regression models with
flexible parameter specification
Diagnostics - Conformal normal curvature-based local influence diagnostics under three perturbation schemes:
Residuals - Randomized quantile residuals
(rcoxsnell(), rqrandomized()) with simulation
envelopes for model validation
Visualization - Publication-quality diagnostic and density plots:
plot_cnc() for local influence plotsenvelope_qq() for quantile-quantile plots with
envelopesplot_evbs_alpha() and plot_evbs_gama() for
parameter density visualizationplot_aggregate_contributions() for influence
aggregationplot_normalized_eigenvalues() for eigenvalue
analysisMonte Carlo Utilities -
generate_evbs_data() and
generate_logevbs_data() for simulation studies
Random Number Generation - revbs()
for generating random variates from EVBS distributions with flexible GEV
parent distributions
The methods implemented in this package are described in: - Ospina, Lima, Barros, and Macedo (2026, submitted)
Application to real-world data: - Monthly maximum wind gust data from
Itajai, Brazil (included in itajai dataset)
For more information, visit: https://raydonal.github.io/evbsreg/