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  "Description": "Implements Dynamic Inner Principal Component Analysis\n(DiPCA), Dynamic Inner Canonical Correlation Analysis (DiCCA),\nand Dynamic Inner Partial Least Squares (DiPLS) for\nmultivariate time series analysis. These methods extract latent\ncomponents that follow autoregressive models, enabling temporal\nprediction and reconstruction. Based on the methods of Dong and\nQin (2018) <doi:10.1016/j.jprocont.2017.05.002> for DiPCA, Dong\nand Qin (2018) <doi:10.1016/j.ifacol.2018.09.379> for DiCCA,\nand Dong and Qin (2018) <doi:10.1016/j.jprocont.2018.04.006>\nfor DiPLS. Integrates with the 'multivarious' package framework\nfor consistent preprocessing and projection interfaces.",
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      "title": "Dynamic-Inner Canonical Correlation Analysis (DiCCA) Extract dynamic latent variables by maximizing correlation between each latent series and its AR(s) prediction (canonical correlation form). This follows Dong & Qin (2018 IFAC) iteration and deflation.",
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        "DiCCA",
        "dicca"
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      "title": "Dynamic Inner PCA (DiPCA)",
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        "dipca",
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        "The Ground Truth: Three Dynamic Latent Components",
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        "Method 1: DiPCA (Dynamic Inner PCA)",
        "How Well Did DiPCA Recover the Components?",
        "DiPCA Properties: Orthogonality",
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