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  "Title": "Average Treatment Effects with Measurement Error and Variable\nSelection for Confounders",
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  "Description": "A recent method proposed by Yi and Chen (2023)\n<doi:10.1177/09622802221146308> is used to estimate the average\ntreatment effects using noisy data containing both measurement\nerror and spurious variables. The package 'AteMeVs' contains a\nset of functions that provide a step-by-step estimation\nprocedure, including the correction of the measurement error\neffects, variable selection for building the model used to\nestimate the propensity scores, and estimation of the average\ntreatment effects. The functions contain multiple options for\nusers to implement, including different ways to correct for the\nmeasurement error effects, distinct choices of penalty\nfunctions to do variable selection, and various regression\nmodels to characterize propensity scores.",
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  "Encoding": "UTF-8",
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  "Packaged": {
    "Date": "2026-05-09 05:51:07 UTC",
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  "Author": "Li-Pang Chen [aut, cre], Grace Yi [aut]",
  "Maintainer": "Li-Pang Chen <lchen723@nccu.edu.tw>",
  "Repository": "https://lchen723.r-universe.dev",
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