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>.
Package details |
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Author | Hossein Haghbin [aut, cre] (<https://orcid.org/0000-0001-8416-2354>), Yue Zhao [aut] (<https://orcid.org/0009-0000-4561-9163>), Mehdi Maadooliat [aut] (<https://orcid.org/0000-0002-5408-2676>) |
Maintainer | Hossein Haghbin <haghbin@pgu.ac.ir> |
License | GPL (>= 2) |
Version | 1.0.0 |
URL | https://github.com/haghbinh/ReMFPCA |
Package repository | View on CRAN |
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