The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or multinomial, and, we can only observe the subject-level outcomes. For example, in manufactory processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The milr package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.

Chen, R.-B., Cheng, K.-H., Chang, S.-M., Jeng, S.-L., Chen, P.-Y., Yang, C.-H., and Hsia, C.-C. (2016). Multiple-Instance Logistic Regression with LASSO Penalty. arXiv:1607.03615 [stat.ML].

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