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cff-version: 1.2.0
message: 'To cite package "ActiveLearning4SPM" in publications use:'
type: software
license: GPL-3.0-only
title: 'ActiveLearning4SPM: Active Learning for Process Monitoring'
version: 0.1.0
doi: 10.32614/CRAN.package.ActiveLearning4SPM
abstract: Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025)
  <https://doi.org/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.
authors:
- family-names: Capezza
  given-names: Christian
  email: christian.capezza@unina.it
- family-names: Lepore
  given-names: Antonio
- family-names: Paynabar
  given-names: Kamran
repository: https://capezza.r-universe.dev
commit: 1005d1af713844d111bdb9dd8cb1d1558cdef29a
date-released: '2025-10-07'
contact:
- family-names: Capezza
  given-names: Christian
  email: christian.capezza@unina.it