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. Since 1.1.0 it also provides the full EVBS distribution family (density, distribution, quantile), the finite upper endpoint and return levels, moving-block bootstrap standard errors, and the corresponding local influence diagnostics for the generalized extreme-value (GEV) regression model, used throughout as a benchmark. Since 1.2.0 it provides evbs_monitor(), a prospective control chart that combines the curvature diagnostic with the finite upper endpoint into an index of endpoint identifiability with a natural control limit. See NEWS.md for the full changelog.
Installation
From a local source tarball:
install.packages("evbsreg_1.2.0.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). gamlss is needed only to fit the optional logEVBS() family with the gamlss package itself.
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(), devbs(), pevbs(), qevbs()
|
EVBS density, distribution, quantile, and random generation |
evbs_endpoint() |
Finite upper endpoint of the fitted model (Weibull domain) |
evbs_return_level() |
Return levels and expected shortfall from the EVBS quantile function |
evbs_block_boot() |
Moving-block bootstrap standard errors for serially dependent series |
evbsreg.fit.mc() |
Monte Carlo simulation study |
plot_evbs_alpha() … |
Density plots (Figures 1–2 of the paper) |
gevreg.fit(), gev_scores(), cnc_diagnostics_gev()
|
GEV regression fit and matching local influence diagnostics |
logEVBS(), dlogEVBS(), plogEVBS(), qlogEVBS()
|
gamlss.family for a log-EVBS model with covariate-dependent shape |
evbs_monitor(), plot.evbs_monitor()
|
Prospective endpoint-identifiability control chart |
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/.