{
  "_id": "6a1d39291d7bb097a0a3e368",
  "Package": "CBPS",
  "Version": "0.24",
  "Date": "2025-11-30",
  "Title": "Covariate Balancing Propensity Score",
  "Authors@R": "c(\nperson(\"Christian\", \"Fong\", email = \"cjfong@umich.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Marc\", \"Ratkovic\", email = \"marc.ratkovic@uni-mannheim.de\", role = c(\"aut\")),\nperson(\"Kosuke\", \"Imai\", email = \"imai@Harvard.Edu\", role = c(\"aut\")),\nperson(\"Chad\", \"Hazlett\", email = \"chazlett@ucla.edu\", role = c(\"ctb\")),\nperson(\"Xiaolin\", \"Yang\", email = \"xiaoliny@princeton.edu\", role = c(\"ctb\")),\nperson(\"Sida\", \"Peng\", email = \"sidpeng@microsoft.com\", role = c(\"ctb\")),\nperson(\"Inbeom\", \"Lee\", email = \"inbeom.lee@chicagobooth.edu\", role = c(\"ctb\"))\n)",
  "Description": "Implements the covariate balancing propensity score (CBPS)\nproposed by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>.\nThe propensity score is estimated such that it maximizes the\nresulting covariate balance as well as the prediction of\ntreatment assignment. The method, therefore, avoids an\niteration between model fitting and balance checking.  The\npackage also implements optimal CBPS from Fan et al. (in-press)\n<DOI:10.1080/07350015.2021.2002159>, several extensions of the\nCBPS beyond the cross-sectional, binary treatment setting. They\ninclude the CBPS for longitudinal settings so that it can be\nused in conjunction with marginal structural models from Imai\nand Ratkovic (2015) <DOI:10.1080/01621459.2014.956872>,\ntreatments with three- and four-valued treatment variables,\ncontinuous-valued treatments from Fong, Hazlett, and Imai\n(2018) <DOI:10.1214/17-AOAS1101>, propensity score estimation\nwith a large number of covariates from Ning, Peng, and Imai\n(2020) <DOI:10.1093/biomet/asaa020>, and the situation with\nmultiple distinct binary treatments administered\nsimultaneously. In the future it will be extended to other\nsettings including the generalization of experimental and\ninstrumental variable estimates.",
  "LazyLoad": "yes",
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  "License": "GPL (>= 2)",
  "NeedsCompilation": "no",
  "RoxygenNote": "7.1.2",
  "Packaged": {
    "Date": "2026-06-01 07:42:25 UTC",
    "User": "root"
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  "Author": "Christian Fong [aut, cre], Marc Ratkovic [aut], Kosuke Imai\n[aut], Chad Hazlett [ctb], Xiaolin Yang [ctb], Sida Peng [ctb],\nInbeom Lee [ctb]",
  "Maintainer": "Christian Fong <cjfong@umich.edu>",
  "Repository": "https://christianfong.r-universe.dev",
  "Date/Publication": "2025-12-01 06:51:29 UTC",
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    "balance",
    "CBIV",
    "CBMSM",
    "CBPS",
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      "class": [
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      "page": "AsyVar",
      "title": "Asymptotic Variance and Confidence Interval Estimation of the ATE",
      "topics": [
        "AsyVar"
      ]
    },
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      "title": "Optimal Covariate Balance",
      "topics": [
        "balance"
      ]
    },
    {
      "page": "balance.CBPS",
      "title": "Calculates the pre- and post-weighting difference in standardized means for covariate within each contrast",
      "topics": [
        "balance.CBPS"
      ]
    },
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      "topics": [
        "balance.CBPSContinuous"
      ]
    },
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      "topics": [
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      ]
    },
    {
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      "title": "Blackwell Data for Covariate Balancing Propensity Score",
      "topics": [
        "Blackwell"
      ]
    },
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      "topics": [
        "CBIV"
      ]
    },
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      "topics": [
        "CBMSM"
      ]
    },
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      "title": "CBMSM.fit",
      "topics": [
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      "title": "Covariate Balancing Propensity Score (CBPS) Estimation",
      "topics": [
        "CBPS"
      ]
    },
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      "topics": [
        "CBPS.fit"
      ]
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      "topics": [
        "hdCBPS",
        "hdCBPS.fit"
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      "topics": [
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