Local Influence Diagnostics for the Extreme-Value Birnbaum-Saunders Regression Model.
evbsreg 
Local influence diagnostics for the Extreme-Value Birnbaum–Saunders (EVBS) regression model.
This package implements the methodology of:
Ospina, R., Lima, J. I. C., Barros, M., and Macêdo, A. M. S. (2026). Local influence diagnostics for the extreme-value Birnbaum–Saunders regression model: methodology, validation, and application to anomalous wind gusts. Submitted.
It provides joint maximum likelihood estimation, conformal normal curvature (CNC) diagnostics under three perturbation schemes, randomized quantile residuals with simulation envelope, Monte Carlo utilities, and publication-quality density and diagnostic plots.
Installation
From a local source tarball:
install.packages("evbsreg_1.0.0.tar.gz", repos = NULL, type = "source")
Or from GitHub:
# install.packages("remotes")
remotes::install_github("Raydonal/evbsreg")
Dependencies: SpatialExtremes (GEV random number generation) and ggplot2 (density plots).
Quick start
library(evbsreg)
data(itajai)
# 1. Fit the EVBS regression model
X <- cbind(1, itajai$pressure)
fit <- evbsreg.fit(X, itajai$wind)
round(fit$coeff, 4)
# 2. Local influence diagnostics
diag <- cnc_diagnostics(fit)
plot_cnc(diag, q = 7)
# 3. Most influential observation
which(diag$Bj[7, ] > diag$bq[7])
# 4. Refit without it and measure the impact
fit82 <- evbsreg.fit(X[-82, ], itajai$wind[-82])
round(100 * (fit82$coeff - fit$coeff) / abs(fit$coeff), 2)
The tail-shape parameter changes by about −73.67% when the catastrophic event of 26 April 2017 (observation 82) is removed, while the regression structure remains stable.
Main functions
| Function | Purpose |
|---|---|
evbsreg.fit() | Joint maximum likelihood fit of the EVBS regression model |
cnc_diagnostics() | Conformal normal curvature diagnostics |
plot_cnc() | Two-panel diagnostic figure (eigenvalues + contributions) |
rqrandomized(), rcoxsnell() | Quantile and Cox–Snell residuals |
envelope_qq() | Normal probability plot with simulation envelope |
revbs() | EVBS random number generation |
evbsreg.fit.mc() | Monte Carlo simulation study |
plot_evbs_alpha() … | Density plots (Figures 1–2 of the paper) |
See vignette("evbsreg") for the full worked example.
Reproducing the paper
Five standalone scripts reproduce every figure, table, and simulation:
source(system.file("scripts/script_01_density_figures.R", package = "evbsreg"))
source(system.file("scripts/script_02_itajai_application.R", package = "evbsreg"))
source(system.file("scripts/script_03_simulation_scenario1.R", package = "evbsreg"))
source(system.file("scripts/script_04_simulation_scenario2.R", package = "evbsreg"))
source(system.file("scripts/script_05_simulation_scenario3.R", package = "evbsreg"))
Each simulation script defaults to m = 5000 replicates (matching the paper). Set m <- 500 at the top of a script for a quick check.
Documentation
Full documentation, including the reference index and the "Get started" vignette, is available at the package website: https://raydonal.github.io/evbsreg/.
License
MIT © Raydonal Ospina.