Nothing

```
#' The milr package: multiple-instance logistic regression with lasso penalty
#'
#' 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.
#'
#' @references
#' \enumerate{
#' \item 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].
#' }
#'
#' @docType package
#' @name milr-package
#' @useDynLib milr
#' @importFrom Rcpp cppFunction sourceCpp
#' @importFrom pipeR %>>%
#' @importFrom utils globalVariables
#' @importFrom RcppParallel RcppParallelLibs
utils::globalVariables(c(".", "%>>%"))
```

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