TieFreeCensor: Algorithm for Generating Tie-Free Progressive Type-II Censored Samples

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).

Version:	0.1.0
Depends:	R (≥ 4.0.0)
Imports:	stats, graphics
Suggests:	testthat (≥ 3.0.0)
Published:	2026-08-21
DOI:	10.32614/CRAN.package.TieFreeCensor (may not be active yet)
Author:	Shikhar Tyagi [aut, cre], Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut]
Maintainer:	Shikhar Tyagi <shikhar1093tyagi at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	TieFreeCensor results

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