Importance Sampling Inference for Censored Univariate Data // 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").