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  "Description": "An index is created using a mathematical model that\ntransforms multi-dimensional variables into a single value.\nThese variables are often correlated, and while PCA-based\nindices can address the issue of multicollinearity, they\ntypically do not account for survey weights, which can lead to\ninaccurate rankings of survey units such as households,\ndistricts, or states. To resolve this, the current package\nfacilitates the development of a principal component\nanalysis-based composite index by incorporating survey weights\nfor each sample observation. This ensures the generation of a\nsurvey-weighted principal component-based normalized composite\nindex. Additionally, the package provides a normalized\nprincipal component-based composite index and ranks the sample\nobservations based on the values of the composite indices. For\nmethod details see, Skinner, C. J., Holmes, D. J. and Smith, T.\nM. F. (1986) <DOI:10.1080/01621459.1986.10478336>, Singh, D.,\nBasak, P., Kumar, R. and Ahmad, T. (2023)\n<DOI:10.3389/fams.2023.1274530>.",
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  "Author": "Pradip Basak [aut, cph, cre], Deepak Singh [aut, cph], Raju\nKumar [aut, cph], Tauqueer Ahmad [aut, cph]",
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