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: grDevices, knitr, rmarkdown, testthat

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