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ActiveLearning4SPM  

Active Learning for Process Monitoring
View on CRAN: Click here


Download and install ActiveLearning4SPM package within the R console
Install from CRAN:
install.packages("ActiveLearning4SPM")

Install from Github:
library("remotes")
install_github("cran/ActiveLearning4SPM")

Install by package version:
library("remotes")
install_version("ActiveLearning4SPM", "0.1.0")



Attach the package and use:
library("ActiveLearning4SPM")
Maintained by
Christian Capezza
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-10-07
Latest Update: 2025-10-07
Description:
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.
How to cite:
Christian Capezza (2025). ActiveLearning4SPM: Active Learning for Process Monitoring. R package version 0.1.0, https://cran.r-project.org/web/packages/ActiveLearning4SPM. Accessed 05 Jun. 2026.
Previous versions and publish date:
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Functions, R codes and Examples using the ActiveLearning4SPM R package
Full ActiveLearning4SPM package functions and examples
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