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  "Title": "Linear Regression and Model Selection Framework",
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  "Author": "Dr. Pramit Pandit [aut, cre], Dr. Bikramjeet Ghose [aut], Dr.\nChiranjit Mazumder [aut]",
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  "Description": "Provides a comprehensive framework for linear regression\nmodeling and associated statistical analysis. The package\nimplements methods for correlation analysis, including\ncomputation of correlation matrices with corresponding\nsignificance levels and visualization via correlation heatmaps.\nIt supports estimation of multiple linear regression models,\nalong with automated model selection through backward\nelimination procedures based on statistical significance\ncriteria. In addition, the package offers a suite of diagnostic\ntools to assess key assumptions of linear regression, including\nmulticollinearity using variance inflation factors,\nheteroscedasticity using the Goldfeld-Quandt test, and\nnormality of residuals using the Shapiro-Wilk test. These\nfunctionalities, as described in Draper and Smith (1998)\n<doi:10.1002/9781118625590>, are designed to facilitate robust\nmodel building, evaluation, and interpretation in applied\nstatistical and data analytical contexts.",
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