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  "Title": "Generalized Kernel Regularized Least Squares",
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  "Encoding": "UTF-8",
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  "License": "GPL (>= 2)",
  "Description": "Kernel regularized least squares, also known as kernel\nridge regression, is a flexible machine learning method. This\npackage implements this method by providing a smooth term for\nuse with 'mgcv' and uses random sketching to facilitate\nscalable estimation on large datasets. It provides additional\nfunctions for calculating marginal effects after estimation and\nfor use with ensembles ('SuperLearning'), double/debiased\nmachine learning ('DoubleML'), and robust/clustered standard\nerrors ('sandwich'). Chang and Goplerud (2024)\n<doi:10.1017/pan.2023.27> provide further details.",
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  "RoxygenNote": "7.3.2",
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  "BugReports": "https://github.com/mgoplerud/gKRLS/issues",
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  "Repository": "https://mgoplerud.r-universe.dev",
  "Date/Publication": "2024-11-07 22:04:39 UTC",
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