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  "Title": "LP Smoothed Inference and Graphics",
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  "Author": "Xiangyu Zhang <zhan6004@umn.edu>, Sara Algeri <salgeri@umn.edu>",
  "Maintainer": "Xiangyu Zhang <zhan6004@umn.edu>",
  "Description": "Classical tests of goodness-of-fit aim to validate the\nconformity of a postulated model to the data under study. In\ntheir standard formulation, however, they do not allow\nexploring how the hypothesized model deviates from the truth\nnor do they provide any insight into how the rejected model\ncould be improved to better fit the data. To overcome these\nshortcomings, we establish a comprehensive framework for\ngoodness-of-fit which naturally integrates modeling,\nestimation, inference and graphics. In this package, the\ndeviance tests and comparison density plots are performed to\nconduct the LP smoothed inference, where the letter L denotes\nnonparametric methods based on quantiles and P stands for\npolynomials. Simulations methods are used to perform variance\nestimation, inference and post-selection adjustments. Algeri S.\nand Zhang X. (2020) <arXiv:2005.13011>.",
  "License": "GPL-3",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-24 07:31:38 UTC",
    "User": "root"
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  "Repository": "https://xiangyu2022.r-universe.dev",
  "Date/Publication": "2020-11-24 10:30:05 UTC",
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