funcharts

The goal of funcharts is to provide control charts for the statistical process monitoring of multivariate functional data densely observed on one-dimensional intervals. The package is thoroughly illustrated in the paper of Capezza et al. (2023). The package provides the methodologies proposed in Colosimo and Pacella (2010), Capezza et al. (2020), Centofanti et al. (2021), Capezza et al. (2024), Centofanti et al. (2024), Capezza et al. (2025), and Centofanti et al. (2025). Moreover, this package provides a new class mfd for multivariate functional data that is a wrapper of the class fd of the package fda. See the vignette("mfd", package = "funcharts").

In particular:

• Colosimo and Pacella (2010) propose control charts for monitoring functional data based on functional principal component analysis. vignette("colosimo2010", package = "funcharts")
• Capezza et al. (2020) propose control charts for monitoring a scalar response variable and functional covariates using scalar-on-function regression. See the vignette("capezza2020", package = "funcharts").
• Centofanti et al. (2021) propose the functional regression control chart (FRCC), i.e. control charts for monitoring a functional response variable conditionally on multivariate functional covariates. See the vignette("centofanti2021", package = "funcharts").
• Capezza et al. (2025) propose the adaptive multivariate functional EWMA (AMFEWMA) control chart.
• Capezza et al. (2024) propose the robust multivariate functional control chart (RoMFCC).
• Centofanti et al. (2024) propose the functional real-time monitoring (FRTM) control chart.
• Centofanti et al. (2025) propose the Adaptive Multivariate Functional Control Chart (AMFCC).

Installation

You can install the CRAN version of the R package funcharts by doing:

install.packages("funcharts")

You can install the development version from GitHub with:

# install.packages("devtools")
# devtools::install_github("unina-sfere/funcharts")
install.packages("funcharts", repos = c('https://unina-sfere.r-universe.dev', 'https://cloud.r-project.org'))

References

• Capezza C., Centofanti F., Lepore A., Menafoglio A., Palumbo B., Vantini S. (2023) funcharts: control charts for multivariate functional data in R. Journal of Quality Technology, doi:10.1080/00224065.2023.2219012.
• Capezza C., Lepore A., Menafoglio A., Palumbo B., Vantini S. (2020) Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar-on-function regression. Applied Stochastic Models in Business and Industry, 36(3):477–500, doi:10.1002/asmb.2507
• Capezza C., Capizzi G., Centofanti F., Lepore A., Palumbo B. (2025) An Adaptive multivariate functional EWMA control chart. Journal of Quality Technology, 57(1), 1-15, doi:https://doi.org/10.1080/00224065.2024.2383674.
• Capezza C., Centofanti F., Lepore A., Palumbo B. (2024) Robust multivariate functional control charts. Technometrics, 66(4):531–547, doi:10.1080/00401706.2024.2327346.
• Centofanti F., Lepore A., Menafoglio A., Palumbo B., Vantini S. (2021) Functional regression control chart. Technometrics, 63(3):281--294, doi:10.1080/00401706.2020.1753581.
• Centofanti F., Lepore A., Kulahci M., and Spooner M. P. (2024). Real-time monitoring of functional data. Accepted for publication in Journal of Quality Technology, doi:10.1080/00224065.2024.2430978.
• Centofanti F., Lepore A., and Palumbo B. (2025). An adaptive multivariate functional control chart. Accepted for publication in Technometrics.
• Colosimo BM, Pacella, M. (2010) A comparison study of control charts for statistical monitoring of functional data. International Journal of Production Research, 48(6), 1575-1601, doi:10.1080/00207540802662888.