ActiveLearning4SPM: Active Learning for Process Monitoring

Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly.

Version: 0.1.0
Depends: R (≥ 4.2)
Imports: Rcpp, Rfast, mvnfast, rrcov, caTools, abind, pROC, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, testthat (≥ 3.0.0)
Published: 2025-10-07
DOI: 10.32614/CRAN.package.ActiveLearning4SPM (may not be active yet)
Author: Christian Capezza [aut, cre], Antonio Lepore [aut], Kamran Paynabar [aut]
Maintainer: Christian Capezza <christian.capezza at unina.it>
License: GPL-3
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: ActiveLearning4SPM results

Documentation:

Reference manual: ActiveLearning4SPM.html , ActiveLearning4SPM.pdf

Downloads:

Package source: ActiveLearning4SPM_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: ActiveLearning4SPM_0.1.0.zip
macOS binaries: r-release (arm64): ActiveLearning4SPM_0.1.0.tgz, r-oldrel (arm64): ActiveLearning4SPM_0.1.0.tgz, r-release (x86_64): ActiveLearning4SPM_0.1.0.tgz, r-oldrel (x86_64): ActiveLearning4SPM_0.1.0.tgz

Linking:

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