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:
Application to real-world data:
itajai dataset)For more information, visit: https://raydonal.github.io/evbsreg/