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  "Description": "Implements the methodology introduced in Capezza, Lepore,\nand Paynabar (2025) <doi:10.1080/00401706.2025.2561744> for\nprocess monitoring with limited labeling resources. The package\nprovides functions to (i) simulate data streams with true\nlatent states and multivariate Gaussian observations as done in\nthe paper, (ii) fit partially hidden Markov models (pHMMs)\nusing a constrained Baum-Welch algorithm with partial labels,\nand (iii) perform stream-based active learning that balances\nexploration and exploitation to decide whether to request\nlabels in real time. The methodology is particularly suited for\nstatistical process monitoring in industrial applications where\nlabeling is costly.",
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