| Type: | Package |
| Title: | Progressive Regularized Vine Copula for Masked Competing Risks |
| Version: | 0.1.0 |
| Description: | Implements Progressive Regularized Vine Copula (Prog-Vine) frameworks for high-dimensional dependent competing risks with masked failure causes under Progressive Type-II Censoring. Fits Weibull marginals, estimates pair-copula trees using Expectation-Maximization (EM) algorithms, computes Louis observed information confidence intervals, and implements Data Augmentation Gibbs Samplers for Bayesian credible intervals. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | stats |
| Config/roxygen2/version: | 8.0.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-08-05 06:57:36 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 08:10:10 UTC |
Weibull Density for Progressive Censoring
Description
Weibull Density for Progressive Censoring
Usage
dweibull_prog(t, alpha, beta)
Arguments
t |
Numeric vector of failure times. |
alpha |
Numeric scale parameter (> 0). |
beta |
Numeric shape parameter (> 0). |
Value
Numeric vector of density values.
Examples
dweibull_prog(c(1.2, 2.5), alpha = 2.0, beta = 1.5)
Fit Progressive Masked Vine Copula via EM Algorithm
Description
Implements Algorithm 5 (EM-Vine MLE Engine) for high-dimensional dependent competing risks with masked causes under Progressive Type-II Censoring.
Usage
fit_em_vine(t, C, R, d = 2, max_iter = 50, tol = 1e-05)
Arguments
t |
Numeric vector of failure times. |
C |
Matrix or list of masking sets (binary matrix or indicator list). |
R |
Numeric vector of progressive removal counts. |
d |
Integer specifying number of competing risks. |
max_iter |
Integer specifying maximum EM iterations. |
tol |
Numeric tolerance convergence criterion. |
Value
List containing estimated Weibull parameters, weights, and log-likelihood.
Examples
set.seed(42)
n <- 20
t_sim <- runif(n, 0.5, 5.0)
R_sim <- rep(0, n)
C_sim <- matrix(sample(c(0, 1), n * 2, replace = TRUE, prob = c(0.3, 0.7)), ncol = 2)
fit <- fit_em_vine(t = t_sim, C = C_sim, R = R_sim, d = 2, max_iter = 10)
print(fit$alpha)
Data Augmentation Gibbs Sampler for Prog-Vine Bayesian Credible Intervals
Description
Implements Algorithm 6 for Prog-Vine Bayesian Inference.
Usage
gibbs_prog_vine(t, C, R, d = 2, n_mc = 100, burn_in = 20)
Arguments
t |
Numeric vector of failure times. |
C |
Binary matrix indicating cause masking sets. |
R |
Vector of progressive removals. |
d |
Integer number of competing risks. |
n_mc |
Total MCMC samples. |
burn_in |
MCMC burn-in length. |
Value
List of posterior chains for alpha and beta parameters.
Examples
set.seed(42)
t_sim <- runif(15, 0.5, 3.0)
R_sim <- rep(0, 15)
C_sim <- matrix(1, nrow = 15, ncol = 2)
gibbs_res <- gibbs_prog_vine(t_sim, C_sim, R_sim, d = 2, n_mc = 30, burn_in = 5)
summary(gibbs_res$alpha_chain)
Weibull Cumulative Distribution for Progressive Censoring
Description
Weibull Cumulative Distribution for Progressive Censoring
Usage
pweibull_prog(t, alpha, beta)
Arguments
t |
Numeric vector of failure times. |
alpha |
Numeric scale parameter (> 0). |
beta |
Numeric shape parameter (> 0). |
Value
Numeric vector of cumulative probability values.
Examples
pweibull_prog(c(1.2, 2.5), alpha = 2.0, beta = 1.5)