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ActiveLearning4SPM

0.1.0

Active Learning for Process Monitoring

0packages depend
2.3Kdownloads / year
66.5%test coverage
13/13checks pass

Overview

About
Maintained by Christian CapezzaFirst published 2025-10-071 releasesCRAN page ↗

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.

Latest changes · 0.1.0

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Health

CRAN checks
13OK
Slowest check: 2.5 min · r-oldrel-windows-x86_64
Check history
  • OK2026-04-22
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

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ActiveLearning4SPM
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Dependencies

Declared dependencies
11 external dependencies (excludes base and recommended)
Depends (1)
R >= 4.2
LinkingTo (2)
Suggests (2)
Enhances (0)
none
Reverse dependencies
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0indirect

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Code & Tests

People & History

People (3)
Maintainer (1)
Author, Maintainer
Authors (3)
Author, Maintainer
Package Timeline

1 release. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.0Latest
    2026-03-10 · current release
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2025-10-07
Total releases
1 / 1 yrs
License
GPL-3 OSI
Download size
26 KB
Installed size
not tracked yet
With dependencies
not tracked yet
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