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  "Title": "Bayesian Modeling and Analysis of Spatially Correlated Survival\nData",
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  "Description": "Provides several Bayesian survival models for\nspatial/non-spatial survival data: proportional hazards (PH),\naccelerated failure time (AFT), proportional odds (PO), and\naccelerated hazards (AH), a super model that includes PH, AFT,\nPO and AH as special cases, Bayesian nonparametric\nnonproportional hazards (LDDPM), generalized accelerated\nfailure time (GAFT), and spatially smoothed Polya tree density\nestimation. The spatial dependence is modeled via frailties\nunder PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and\nPH. Model choice is carried out via the logarithm of the pseudo\nmarginal likelihood (LPML), the deviance information criterion\n(DIC), and the Watanabe-Akaike information criterion (WAIC).\nSee Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.",
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