Importance Sampling Inference for Censored Univariate Data R package UniIS version 0.1.0 Distribution-independent framework for importance-sampling inference with univariate observations subject to censoring or truncation. Users provide probability functions and a proposal over model parameters. Constructs observed-data likelihood contributions, computes numerically stable importance weights, and supplies posterior, likelihood, predictive, diagnostic, and model-comparison summaries. Covers complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, progressive first-failure, doubly Type-II, middle-censored, and left/right-truncated data. Methods for importance sampling and censoring schemes are described in Geweke (1989) <doi:10.2307/2290062>, Hesterberg (1995) <doi:10.1080/00031305.1995.10476138>, Robert and Casella (2004, ISBN:978-0-387-21617-1), Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>, Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>, Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>, Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>, Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>, Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>, Balakrishnan and Aggarwala (2000, ISBN:980-1-4612-1334-5), Ding and Gui (2023) <doi:10.3390/math11092003>, Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>, Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>, Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>, and Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"). Software Depends: R (>= 4.1.0) Imports: stats, graphics Suggests: testthat (>= 3.0.0), knitr, rmarkdown Shikhar Tyagi <shikhar1093tyagi@gmail.com> Comprehensive R Archive Network (CRAN) Shikhar Tyagi [aut, cre] (ORCID: <https://orcid.org/0000-0003-1606-0844>), Arvind Pandey [aut], Bhupendra Singh [aut], Vrijesh Tripathi [aut] GPL-3 2026-08-06 application/tgz https://CRAN.R-project.org/package=UniIS doi:10.32614/CRAN.package.UniIS en-US