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  "Title": "Mean Squared Out-of-Sample Error Projection",
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  "Authors@R": "person(\"Chris\", \"Rohlfs\", , \"car2228@columbia.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-7714-9231\"))",
  "Description": "Projects mean squared out-of-sample error for a linear\nregression based upon the methodology developed in Rohlfs\n(2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs\nthe lm object from an estimated OLS regression (based on the\n\"training sample\") and a data.frame of out-of-sample cases (the\n\"test sample\") that have non-missing values for the same\npredictors. The test sample may or may not include data on the\noutcome variable; if it does, that variable is not used. The\naim of the exercise is to project what what mean squared\nout-of-sample error can be expected given the predictor values\nsupplied in the test sample. Output consists of a list of three\nelements: the projected mean squared out-of-sample error, the\nprojected out-of-sample R-squared, and a vector of\nout-of-sample \"hat\" or \"leverage\" values, as defined in the\npaper.",
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  "Author": "Chris Rohlfs [aut, cre]\n(<https://orcid.org/0000-0001-7714-9231>)",
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    "description": "Exploring AI-based predictive modeling, causal inference, and model generalizability to decode complex systems like human preferences. See my papers on arXiv 👇"
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