Algorithm for Generating Tie-Free Progressive Type-II Censored Samples
Overview
Generates tie-free progressive Type-II censored samples from discrete distributions and user-specified discrete probability mass functions (PMF) or cumulative distribution functions (CDF). Provides maximum likelihood estimation (MLE), Bayesian estimation via Markov chain Monte Carlo (MCMC) Metropolis-within-Gibbs sampling, likelihood-based parametric bootstrap goodness-of-fit (GOF) tests, profile log-likelihood diagnostics, and discrete survival and probability calculations. Methods are based on Ahmad and Mansour (2026) doi:10.1155/jom/3657078, Balakrishnan and Dembinska (2008) doi:10.1016/j.jspi.2007.02.006, Joe and Zhu (2005) doi:10.1002/bimj.200410102, and Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5).
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK7 OK2026-08-22
Documentation
- Help pages
- 21
- Examples that run
- 89%8 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 38%
Downloads
Dependencies
Installing TieFreeCensor adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
- 0.1.0Latest2026-08-21 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-21
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.0.0
- Bundled data
- 0.4 KB / 2 files
- Download size
- 19 KB
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