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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- badges: start -->
[![CRAN status](https://www.r-pkg.org/badges/version/funcharts)](https://CRAN.R-project.org/package=funcharts)
[![R-CMD-check](https://github.com/unina-sfere/funcharts/workflows/R-CMD-check/badge.svg)](https://github.com/unina-sfere/funcharts/actions)
[![Codecov test coverage](https://codecov.io/gh/unina-sfere/funcharts/branch/main/graph/badge.svg)](https://app.codecov.io/gh/unina-sfere/funcharts?branch=main)
<!-- badges: end -->
# 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. (2024a), and Capezza et al. (2024b).
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")`](https://unina-sfere.github.io/funcharts/articles/mfd.html).
In particular:
* Colosimo and Pacella (2010) proposed control charts for monitoring functional data based on functional principal component analysis. [`vignette("colosimo2010", package = "funcharts")`](https://unina-sfere.github.io/funcharts/articles/colosimo2010.html)
* Capezza et al. (2020) proposed control charts for monitoring a scalar response variable and functional covariates
using scalar-on-function regression. See the [`vignette("capezza2020", package = "funcharts")`](https://unina-sfere.github.io/funcharts/articles/capezza2020.html).
* Centofanti et al. (2021) proposed the functional regression control chart, i.e. control charts for monitoring a functional response variable conditionally on multivariate functional covariates.
See the [`vignette("centofanti2021", package = "funcharts")`](https://unina-sfere.github.io/funcharts/articles/centofanti2021.html).
* Capezza et al. (2024a) proposed the adaptive multivariate functional EWMA control chart.
* Capezza et al. (2024b) proposed the robust multivariate functional control chart.
## Installation
You can install the CRAN version of the R package `funcharts` by doing:
```{r, eval = FALSE}
install.packages("funcharts")
```
You can install the development version from GitHub with:
```{r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("unina-sfere/funcharts")
```
# 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 CO<sub>2</sub> 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. (2024a) An Adaptive Multivariate Functional EWMA Control Chart. Accepted for publication in *Journal of Quality Technology*.
* Capezza, C., Centofanti, F., Lepore, A., Palumbo, B. (2024b) Robust Multivariate Functional Control Charts. *Technometrics*, 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
* 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