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 categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing 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.

Maintainer: Ping-Yang Chen <pychen.ping at gmail.com>

Author(s): Ping-Yang Chen*, ChingChuan Chen*, Chun-Hao Yang*, Sheng-Mao Chang*

Install package and any missing dependencies by running this line in your R console:

install.packages("milr")

Depends R (>= 3.2.3)
Imports utils, pipeR(>=0.5), numDeriv, glmnet, Rcpp(>=0.12.0), RcppParallel
Suggests testthat, knitr, knitcitations, rmarkdown, data.table, ggplot2, plyr
Enhances
Linking to Rcpp, RcppArmadillo, RcppParallel
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Package milr
Materials
URL https://github.com/PingYangChen/milr
Task Views
Version 0.3.0
Published 2017-06-08
License MIT + file LICENSE
BugReports https://github.com/PingYangChen/milr/issues
SystemRequirements GNU make
NeedsCompilation yes
Citation
CRAN checks milr check results
Package source milr_0.3.0.tar.gz