Functional Control Charts
Get out of control observations from control charts
Get outliers from multivariate functional data
Plot control charts
Use a function-on-function linear regression model for prediction
Finds functional componentwise outliers
Define a Multivariate Functional Data Object
Robust Multivariate Functional Data Imputation (RoMFDI)
Robust multivariate functional principal components analysis
Adaptive Multivariate Functional EWMA control chart - Phase I
Adaptive Multivariate Functional EWMA control chart - Phase II
Bind variables of two Multivariate Functional Data Objects
Produce contribution plots
Real-time T2 and SPE control charts for multivariate functional data
T2 and SPE control charts for multivariate functional data
Real-time scalar-on-function regression control charts
Control charts for monitoring a scalar quality characteristic adjusted...
Correlation Function for Multivariate Functional Data
Covariance Function for Multivariate Functional Data
Simulate multivariate functional data
Get a list of function-on-function linear regression models estimated ...
Function-on-function linear regression based on principal components
Phase I of the FRTM method.
Phase II of the FRTM method.
funcharts: Functional Control Charts
Get a list of functional data objects each evolving up to an intermedi...
Get Multivariate Functional Data from a three-dimensional array
Get a list of functional data objects each evolving up to an intermedi...
Get Multivariate Functional Data from a data frame
Convert a fd object into a Multivariate Functional Data object.
Get a list of functional data objects each evolving up to an intermedi...
Get Multivariate Functional Data from a list of matrices
Get possible outliers of a training data set of a scalar-on-function r...
Inner product of two multivariate functional data objects, for each ob...
Inner products of functional data contained in mfd objects.
Confirm Object has Class mfd
Add the plot of a new multivariate functional data object to an existi...
Mean Function for Multivariate Functional Data
Mixed Functional Principal Component Analysis (mFPCA)
Norm of Multivariate Functional Data
Open-end/open-begin Functional Dynamic Time Warping (OEB-FDTW)
Setting open-end/open-begin functional dynamic time warping (OEB-FDTW)...
Setting mixed functional principal component analysis (mFPCA) defaults
Setting real-time registration step defaults
Get a list of multivariate functional principal component analysis mod...
Multivariate functional principal components analysis
Plot a Bivariate Functional Data Object.
Plot bootstrapped estimates of the scalar-on-function regression coeff...
Plot real-time control charts
Plot a Multivariate Functional Data Object.
Plot multivariate functional object over the training data set
Plot the harmonics of a pca_mfd object
Plot the results of the Phase I and the Phase II of the FRTM
Plot the results of the Mixed Functional Principal Component Analysis ...
Use a scalar-on-function linear regression model for prediction
Bind replications of two Multivariate Functional Data Objects
Real-time functional regression control chart
Functional Regression Control Chart
Real-time Scalar-on-Function Regression Control Chart
Scalar-on-Function Regression Control Chart
Robust Multivariate Functional Control Charts - Phase I
Robust Multivariate Functional Control Charts - Phase II
Standardize Multivariate Functional Data.
Simulate example data for funcharts
Simulate data for real-time monitoring of univariate functional data
Simulate a data set for funcharts
Get a list of scalar-on-function linear regression models estimated on...
Scalar-on-function linear regression based on principal components
Extract observations and/or variables from mfd objects.
Tensor product of two Multivariate Functional Data objects
Get the index of the out of control observations from control charts
Provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>, Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>, Capezza et al. (2024) <doi:10.1080/00224065.2024.2383674>, Centofanti et al. (2022) <doi:10.48550/arXiv.2205.06256>. The package is thoroughly illustrated in the paper of Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.
Useful links