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ReMFPCA  

Regularized Multivariate Functional Principal Component Analysis
View on CRAN: Click here


Download and install ReMFPCA package within the R console
Install from CRAN:
install.packages("ReMFPCA")

Install from Github:
library("remotes")
install_github("cran/ReMFPCA")

Install by package version:
library("remotes")
install_version("ReMFPCA", "1.0.0")



Attach the package and use:
library("ReMFPCA")
Maintained by
Hossein Haghbin
[Scholar Profile | Author Map]
First Published: 2023-07-01
Latest Update: 2023-07-01
Description:
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) .
How to cite:
Hossein Haghbin (2023). ReMFPCA: Regularized Multivariate Functional Principal Component Analysis. R package version 1.0.0, https://cran.r-project.org/web/packages/ReMFPCA. Accessed 20 Feb. 2025.
Previous versions and publish date:
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Complete documentation for ReMFPCA
Functions, R codes and Examples using the ReMFPCA R package
Full ReMFPCA package functions and examples
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