ActiveLearning4SPM
0.1.0Active Learning for Process Monitoring
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 75%
- Documented parameters
- 94%
- Return-value docs
- 100%
- References docs
- 80%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-10-07
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.2
- Download size
- 26 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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