                        Changes in version 1.2.1                        

Bug fixes and documentation

  - 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).

New features

  - 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.

                 Changes in version 1.2.0 (2026-09-18)                  

Bug fixes

  - 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.

New features

  - 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.

                        Changes in version 1.1.0                        

New features

  - 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.

Important notes

  - The extreme-value index of the EVBS response is 2 * gama, not gama.
    The transformation preserves the max-domain of attraction but
    doubles the tail index. Documentation updated accordingly.
  - The GEV return-level formula does not apply to the EVBS response.
    Use 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.

                 Changes in version 1.0.0 (2026-06-30)                  

New Features

  - 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:
    
      - Case-weight perturbation
      - Response variable perturbation
      - Explanatory variable perturbation

  - Residuals - Randomized quantile residuals (rcoxsnell(),
    rqrandomized()) with simulation envelopes for model validation

  - Visualization - Publication-quality diagnostic and density plots:
    
      - plot_cnc() for local influence plots
      - envelope_qq() for quantile-quantile plots with envelopes
      - plot_evbs_alpha() and plot_evbs_gama() for parameter density
        visualization
      - plot_aggregate_contributions() for influence aggregation
      - plot_normalized_eigenvalues() for eigenvalue analysis

  - Monte 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

Methodology

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)

Documentation

  - Complete function reference with examples
  - Comprehensive vignette demonstrating workflow on real data
  - CITATION file with proper attribution

Dependencies

  - Imports: stats, graphics, SpatialExtremes, ggplot2
  - Suggests: gamlss, grDevices, knitr, rmarkdown, testthat

For more information, visit: https://raydonal.github.io/evbsreg/
