Package: milr
Type: Package
Title: Multiple-Instance Logistic Regression with LASSO Penalty
Version: 0.2.0
Date: 2017-01-02
Description: 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.  
Authors@R: c(person("Ping-Yang", "Chen", , "pychen.ping@gmail.com", c("aut", "cre")),
             person("ChingChuan", "Chen", , "celestial0230@stat.sinica.edu.tw", "aut"), 
             person("Chun-Hao", "Yang", , "chunhaoyang@ufl.edu", "aut"),
             person("Sheng-Mao", "Chang", , "smchang@mail.ncku.edu.tw", "aut"))
Author: Ping-Yang Chen [aut, cre], 
    ChingChuan Chen [aut], 
    Chun-Hao Yang [aut], 
    Sheng-Mao Chang [aut]
URL: https://github.com/PingYangChen/milr
BugReports: https://github.com/PingYangChen/milr/issues
Maintainer: Ping-Yang Chen <pychen.ping@gmail.com>
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
LazyData: yes
License: MIT + file LICENSE
Collate: 'RcppExports.R' 'milr-package.R' 'DGP.R' 'softmax.R' 'milr.R'
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2017-01-06 05:54:12 UTC; stat
Repository: CRAN
Date/Publication: 2017-01-10 19:03:54
