envelope_qq() did not restore the graphical parameter
pty on exit: it saved par("pty") without its
name, so par(oldpar) restored nothing and warned ‘“m” is
not a graphical parameter’. It now uses
oldpar <- par(pty = "s"); on.exit(par(oldpar)). A test
checks that pty is restored and that no warning is
emitted.envelope_qq() failed for nrep below 40 (an
order-statistic index of 0). The indices are now kept valid; the bands
are unchanged for nrep >= 40.SpatialExtremes::rgev(), inside revbs(); it is
replaced by an internal generator built on the same inversion of an
Exp(1) draw, so revbs() and evbsreg.fit.mc()
return identical values for a given seed.envelope_qq() envelope, evbs_block_boot()
resampling) now run after set.seed(), and numbers quoted in
the vignette text are computed inline.evbs_monitor(), and the comparison with GEV regression
using the common statistic Bbar.DESCRIPTION, README.md and
inst/CITATION updated to the current title of the
accompanying paper, with the arXiv preprint; the version history was
removed from the Description field.favicon.svg, favicon-96x96.png,
site.webmanifest), which were missing.devbs() documentation stated that the extreme-value
index of the response is always 2*gamma. That holds only for gamma >
0; for gamma <= 0 the index is gamma itself (Ferreira, Gomes and
Leiva, 2012, Theorems 4.1-4.2).evbs_return_level() documentation now states that the
level is conditional on the covariate value, that period is
in units of one observation (months, for monthly maxima), and what
reading it as an annual return level assumes.evbs_monitor() documentation now states that the
endpoint is evaluated at the covariate value of the running maximum, as
the code does.inst/scripts/: the simulation scripts used gamma in
{-0.2, -0.1, 0.1, 0.2} whereas the paper’s tables use {-0.2, 0, 0.2};
Scenario 3 used alpha = 0.5, which with the contamination alpha - 0.5
gave a degenerate alpha = 0. Both now match the paper (Scenario 3: beta
= (1, 2), alpha = 1.2).cnc_diagnostics_gev() also returns Bbar,
the mean aggregate contribution computed exactly as
colMeans(cnc_diagnostics(fit)$Bj) is for the EVBS model, so
that influence rankings can be compared across the two families. The
existing Bj (normalised diagonal of the influence matrix)
is unchanged.gev_monitor() computes the prospective identifiability
index of evbs_monitor() under the GEV regression.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/