UniIS: 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").
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[aut, cre],
Arvind Pandey [aut],
Bhupendra Singh [aut],
Vrijesh Tripathi [aut]