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  "Description": "Estimation, inference, and quantities of interest for\nordered probit and ordered logit models whose outcome contains\nan inflated category: a single ordered category (bottom,\nmiddle, top, or any other) that mixes observations generated by\nthe ordered process with observations generated by a distinct\nsplit-population process. Fits the zero-inflated ordered probit\nof Harris and Zhao (2007) <doi:10.1016/j.jeconom.2007.01.002>\nand its middle- and top-inflated extensions (Bagozzi and\nMukherjee 2012 <doi:10.1093/pan/mps020>; Bagozzi, Hill, Moore\nand Mukherjee 2015 <doi:10.1177/0022002713520530>; Bagozzi, Joo\nand Mukherjee 2024 <doi:10.1093/fpa/orae006>), generalized to\nan arbitrary inflated category and to the logit link, with\noptional correlated errors for the probit form, plus the\nstandard ordered probit and logit and their partial\nproportional-odds (non-parallel) variants on the same footing.\nProvides analytic, robust, and cluster-robust standard errors,\nsurvey weights and offsets, model comparison (Vuong,\nlikelihood-ratio, information criteria), regime-specific\npredicted probabilities and first differences, simulation for\nresidual diagnostics, and tidy/table-package integration. The\nlikelihood, its gradient, and the bivariate-normal\nprobabilities are implemented in C++.",
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