| Type: | Package |
| Title: | Distributionally Robust Progressive Type-II Censoring Inference |
| Version: | 0.1.0 |
| Description: | Implements distributionally robust optimization algorithms for progressive Type-II censored Weibull lifetime data under Wasserstein ambiguity balls. Provides primal-dual convex optimization solvers and robust inference bounds. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Config/roxygen2/version: | 8.0.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-08-05 07:37:30 UTC; Dr. O. J. Obulezi |
| Author: | Okechukwu J. Obulezi [aut, cre] |
| Maintainer: | Okechukwu J. Obulezi <oj.obulezi@unizik.edu.ng> |
| Repository: | CRAN |
| Date/Publication: | 2026-08-09 08:00:17 UTC |
Wasserstein-Bounded Robust Bayesian Bounds
Description
Computes upper and lower robust inference bounds across extreme prior profiles.
Usage
dro_bayesian_bounds(t, R, delta = 0.1)
Arguments
t |
Numeric vector of observed failure/censoring times. |
R |
Numeric vector of progressive removal counts. |
delta |
Numeric prior ambiguity radius (default: 0.1). |
Value
Matrix of lower and upper robust credible bounds.
Examples
t_obs <- sort(rweibull(10, shape = 2, scale = 8))
R_obs <- rep(0, 10)
bounds <- dro_bayesian_bounds(t = t_obs, R = R_obs, delta = 0.05)
print(bounds)
Primal-Dual Convex Optimization Solver for DRO-PC Estimator
Description
Solves worst-case distributionally robust MLE parameter estimates under Wasserstein ambiguity balls for progressive Type-II censored Weibull data.
Usage
fit_dro_pc(t, R, epsilon = 0.05, max_iter = 500, tol = 1e-05)
Arguments
t |
Numeric vector of observed failure/censoring times. |
R |
Numeric vector of progressive removal counts. |
epsilon |
Numeric radius of the Wasserstein ambiguity ball (default: 0.05). |
max_iter |
Integer maximum number of iterations (default: 500). |
tol |
Numeric convergence tolerance (default: 1e-5). |
Value
A list containing estimated robust parameters, dual multiplier, and covariance.
Examples
set.seed(123)
t_obs <- sort(rweibull(15, shape = 2, scale = 10))
R_obs <- c(2, rep(0, 13), 3)
fit <- fit_dro_pc(t = t_obs, R = R_obs, epsilon = 0.02)
print(fit$par)