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 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.
| Version: |
0.3.1 |
| Depends: |
R (≥ 3.2.3) |
| Imports: |
utils, pipeR (≥ 0.5), numDeriv, glmnet, Rcpp (≥ 0.12.0), RcppParallel |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppParallel |
| Suggests: |
testthat, knitr, Hmisc, rmarkdown, data.table, ggplot2, plyr |
| Published: |
2020-10-31 |
| 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 |
| SystemRequirements: |
GNU make |
| CRAN checks: |
milr results |
Documentation:
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