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  "Title": "Regularized Multivariate Functional Principal Component Analysis",
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  "Authors@R": "c(\nperson(\"Hossein\", \"Haghbin\", email = \"haghbin@pgu.ac.ir\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-8416-2354\")),\nperson(\"Yue\", \"Zhao\", email = \"yue.zhao@mu.edu\", role = \"aut\", comment = c(ORCID = \"0009-0000-4561-9163\")),\nperson(\"Mehdi\", \"Maadooliat\", email = \"mehdi.maadooliat@mu.edu\", role = \"aut\", comment = c(ORCID = \"0000-0002-5408-2676\"))\n)",
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  "Description": "Methods and tools for implementing regularized\nmultivariate functional principal component analysis\n('ReMFPCA') for multivariate functional data whose variables\nmight be observed over different dimensional domains. 'ReMFPCA'\nis an object-oriented interface leveraging the extensibility\nand scalability of R6. It employs a parameter vector to control\nthe smoothness of each functional variable. By incorporating\nsmoothness constraints as penalty terms within a regularized\noptimization framework, 'ReMFPCA' generates smooth multivariate\nfunctional principal components, offering a concise and\ninterpretable representation of the data. For detailed\ninformation on the methods and techniques used in 'ReMFPCA',\nplease refer to Haghbin et al. (2023)\n<doi:10.48550/arXiv.2306.13980>.",
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    },
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      "page": "sub-.mvmfd",
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