Regularized Multivariate Functional Principal Component Analysis
Define a Set of Multidimensional Functional Basis
Bivariate plot for mvmfd objects
Subtract two mfd objects
Subtraction of two mvmfd objects
Compute the inner product between two objects of class mfd
Compute the inner product between two objects of class mvmfd
Check if an object is of class 'basismfd'
Check if an object is of class 'mfd'
Check if an object is of class 'mvbasismfd'
Check if an object is of class 'mvmfd'
Length of an object of classes mfdor mvmfd.
mean of an object of classes mfdor mvmfd.
Define a Set of Multidimensional Functional Data objects
Define a Set of Multivariate Multidimensional Functional Basis
Define a Set of Multivariate Multidimensional Functional Data objects
Compute the norm of an object of class mfd
Compute the norm of an object of class mvmfd
Penalty Function
plots an object of classes mfd, mvmfd or remfpca
Add two mfd objects
Addition of two mvmfd objects
A Class for ReMFPCA objects
Standard deviation of an object of class mfd.
Extract subsets of an mfd object
Extract subsets of an mvmfd object
Scalar multiplication of an mfd object
Multiplication of an mvmfd object with a scalar
Methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. 'ReMFPCA' is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, 'ReMFPCA' generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in 'ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.