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  "Title": "Multiblock Sparse Multivariable Analysis",
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  "Description": "Several functions can be used to analyze multiblock\nmultivariable data. If the input is a single matrix, then\nprincipal components analysis (PCA) is implemented. If the\ninput is a list of matrices, then multiblock PCA is\nimplemented. If the input is two matrices, for exploratory and\nobjective variables, then partial least squares (PLS) analysis\nis implemented. If the input is two lists of matrices, for\nexploratory and objective variables, then multiblock PLS\nanalysis is implemented. Additionally, if an extra outcome\nvariable is specified, then a supervised version of the methods\nabove is implemented. For each method, sparse modeling is also\nincorporated. Functions for selecting the number of components\nand regularized parameters are also provided. Version 4.0 adds\nopt-in supervised sparse soft-structured principal component\nanalysis, reconstruction, and repeated split\nreconstruction-based parameter selection while preserving the\ndefault Version 3.2 computational paths.",
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  "Encoding": "UTF-8",
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  "Date/Publication": "2026-09-20 13:04:35 UTC",
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