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  "Description": "The multiple instance data set consists of many\nindependent subjects (called bags) and each subject is composed\nof several components (called instances). The outcomes of such\ndata set are binary or categorical responses, and, we can only\nobserve the subject-level outcomes. For example, in\nmanufacturing processes, a subject is labeled as \"defective\" if\nat least one of its own components is defective, and otherwise,\nis labeled as \"non-defective\". The 'milr' package focuses on\nthe predictive model for the multiple instance data set with\nbinary outcomes and performs the maximum likelihood estimation\nwith the Expectation-Maximization algorithm under the framework\nof logistic regression. Moreover, the LASSO penalty is attached\nto the likelihood function for simultaneous parameter\nestimation and variable selection.",
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