Package: milr 0.3.1

Ping-Yang Chen

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.

Authors:Ping-Yang Chen [aut, cre], ChingChuan Chen [aut], Chun-Hao Yang [aut], Sheng-Mao Chang [aut]

milr_0.3.1.tar.gz
milr_0.3.1.tar.gz(r-4.5-noble)milr_0.3.1.tar.gz(r-4.4-noble)
milr_0.3.1.tgz(r-4.4-emscripten)milr_0.3.1.tgz(r-4.3-emscripten)
milr.pdf |milr.html
milr/json (API)

# Install 'milr' in R:
install.packages('milr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/pingyangchen/milr/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

openblascpp

2.11 score 13 scripts 240 downloads 3 exports 14 dependencies

Last updated 4 years agofrom:55e4ab3cfa. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linux-x86_64NOTEDec 02 2024

Exports:DGPmilrsoftmax

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixnumDerivpipeRRcppRcppArmadilloRcppEigenRcppParallelshapesurvival

milr: Multiple-Instance Logistic Regression with Lasso Penalty

Rendered frommilr-intro.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2020-10-31
Started: 2017-06-08