Other packages > Find by keyword >

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 06 Mar. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited ActiveLearning4SPM R package
View ActiveLearning4SPM citation profile
Other R packages that ActiveLearning4SPM depends, imports, suggests or enhances
Complete documentation for ActiveLearning4SPM
Functions, R codes and Examples using the ActiveLearning4SPM R package
Full ActiveLearning4SPM package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

solitude  
An Implementation of Isolation Forest
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detecti ...
Download / Learn more Package Citations See dependency  
openxlsx  
Read, Write and Edit xlsx Files
Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, stylin ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
EMVS  
The Expectation-Maximization Approach to Bayesian Variable Selection
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization p ...
Download / Learn more Package Citations See dependency  
mlr3viz  
Visualizations for 'mlr3'
Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, le ...
Download / Learn more Package Citations See dependency  
lbfgs  
Limited-memory BFGS Optimization
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implem ...
Download / Learn more Package Citations See dependency  

26,264

R Packages

223,360

Dependencies

70,376

Author Associations

26,265

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA