milr: 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.

Version: 0.2.0
Depends: R (≥ 3.2.3)
Imports: assertthat, pipeR (≥ 0.5), numDeriv, purrr (≥ 0.2.0), Rcpp (≥ 0.12.0), glmnet, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat
Published: 2017-01-10
Author: Ping-Yang Chen [aut, cre], ChingChuan Chen [aut], Chun-Hao Yang [aut], Sheng-Mao Chang [aut]
Maintainer: Ping-Yang Chen <pychen.ping at gmail.com>
BugReports: https://github.com/PingYangChen/milr/issues
License: MIT + file LICENSE
URL: https://github.com/PingYangChen/milr
NeedsCompilation: yes
CRAN checks: milr results

Downloads:

Reference manual: milr.pdf
Package source: milr_0.2.0.tar.gz
Windows binaries: r-devel: milr_0.2.0.zip, r-release: milr_0.2.0.zip, r-oldrel: milr_0.2.0.zip
OS X Mavericks binaries: r-release: milr_0.2.0.tgz, r-oldrel: milr_0.2.0.tgz
Old sources: milr archive

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