Title: Multiple Imputation for Multivariate Binary Outcome
Author: Dadong Zhang, Josyf C. Mychaleckyj
Description: Multiple imputation for multivariate binary outcome by using Gibbs' Sampler on all potential profiles.

Title: Mixed Logistic Regression for Genome-Wide Analysis Studies
Author: Hervé Perdry and Jacqueline Milet
Description: Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Milet et al 2020 <doi:10.1101/2020.01.17.910109>.

Title: Multiple-Instance Logistic Regression with LASSO Penalty
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"))
Imports: assertthat, pipeR (>= 0.5), numDeriv, purrr (>= 0.2.0), Rcpp
Collate: 'RcppExports.R' 'milr-package.R' 'DGP.R' 'softmax.R' 'milr.R'

labeled as "non-defective".  The 'milr' package focuses on the predictive model for the

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 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.
Imports: utils, pipeR (>= 0.5), numDeriv, glmnet, Rcpp (>= 0.12.0),
Suggests: testthat, knitr, knitcitations, rmarkdown, data.table,