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milr
View on CRAN: Click
here
Download and install milr package within the R console
Install from CRAN:
install.packages("milr")
Install from Github:
library("remotes")
install_github("cran/milr")
Install by package version:
library("remotes")
install_version("milr", "0.3.1")
Attach the package and use:
library("milr")
Maintained by
Ping-Yang Chen
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-07-14
Latest Update: 2020-10-31
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.
How to cite:
Ping-Yang Chen (2016). milr: Multiple-Instance Logistic Regression with LASSO Penalty. R package version 0.3.1, https://cran.r-project.org/web/packages/milr. Accessed 22 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for milr
Functions, R codes and Examples using
the milr R package
Some associated functions: DGP . fitted.milr . fitted.softmax . logit . milr-package . milr . predict.milr . predict.softmax . softmax .
Some associated R codes: DGP.R . RcppExports.R . milr-package.R . milr.R . softmax.R . Full milr package functions and examples
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