{ "_id": "6a0f6832acfb0bcc41c5da2a", "Type": "Package", "Package": "minb", "Title": "Multiple-Inflated Negative Binomial Model", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Yang\", \"Li\", role=c(\"aut\"), email=\"yang.li@ruc.edu.cn\"),\nperson(\"Mingcong\", \"Wu\", role=c(\"aut\", \"cre\"), email=\"wumingcong@ruc.edu.cn\"),\nperson(\"Mengyun\", \"Wu\", role=c(\"aut\"), email=\"wu.mengyun@shufe.edu.cn\"),\nperson(\"Shuangge\", \"Ma\", role=c(\"aut\"), email=\"shuangge.ma@yale.edu\"))", "Description": "Count data is prevalent and informative, with widespread\napplication in many fields such as social psychology,\npersonality, and public health. Classical statistical methods\nfor the analysis of count outcomes are commonly variants of the\nlog-linear model, including Poisson regression and Negative\nBinomial regression. However, a typical problem with count data\nmodeling is inflation, in the sense that the counts are\nevidently accumulated on some integers. Such an inflation\nproblem could distort the distribution of the observed counts,\nfurther bias estimation and increase error, making the classic\nmethods infeasible. Traditional inflated value selection\nmethods based on histogram inspection are easy to neglect true\npoints and computationally expensive in addition. Therefore, we\npropose a multiple-inflated negative binomial model to handle\ncount data modeling with multiple inflated values, achieving\ndata-driven inflated value selection. The proposed approach\nprovides simultaneous identification of important regression\npredictors on the target count response as well. More details\nabout the proposed method are described in Li, Y., Wu, M., Wu,\nM., & Ma, S. 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