evbsreg 1.0.0

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/