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  "Title": "Advanced Methods for Principal Component Analysis and Principal\nComponent Regression",
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  "Authors@R": "c(person(\"Dr. Pramit\", \"Pandit\", role = c(\"aut\", \"cre\"), email = \"pramitpandit@gmail.com\"),\nperson(\"Dr. Halagundegowda\", \"G R\", role = \"aut\"),\nperson(\"Dr. Kamidi\", \"Rahul\", role = \"aut\"),\nperson(\"Dr. S. Gandhi\", \"Doss\", role = \"aut\"))",
  "Author": "Dr. Pramit Pandit [aut, cre], Dr. Halagundegowda G R [aut], Dr.\nKamidi Rahul [aut], Dr. S. Gandhi Doss [aut]",
  "Description": "Provides a unified framework for principal component\nanalysis (PCA) and principal component regression (PCR),\nincluding standard PCA, sparse PCA, robust PCA, and supervised\nPCA. The package supports automatic selection of the number of\ncomponents using cumulative variance and elbow methods and\nintegrates PCA with regression modelling through PCR models. It\nincludes tools for PCA suitability assessment using Bartlett's\ntest of sphericity and the Kaiser-Meyer-Olkin (KMO) measure.\nVisualisation utilities such as scree plots and biplots are\nprovided for interpretation. The methods are designed to handle\nmulticollinearity, outliers, and high-dimensional data, making\nthem suitable for applied statistical modelling and data\nanalysis. The methodology is based on established approaches\ndescribed in Jolliffe (2002) <doi:10.1007/b98835>, Zou et al.\n(2006) <doi:10.1111/j.1467-9868.2005.00503.x>, and Hubert et\nal. (2005) <doi:10.1198/004017004000000563>.",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-05-31 15:00:08 UTC",
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