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 |
| 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:
Downloads:
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