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  "Title": "Kernel Balancing",
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  "Date": "2026-02-28",
  "Authors@R": "c(person(\"Chad\", \"Hazlett\", email = \"chazlett@ucla.edu\", role = c(\"aut\", \"cph\")),\nperson(\"Ciara\", \"Sterbenz\", email = \"cster@g.ucla.edu\", role = \"aut\"),\nperson(\"Erin\", \"Hartman\", email = \"ekhartman@ucla.edu\", role = \"ctb\"),\nperson(\"Alex\", \"Kravetz\", email = \"alexdkravetz@gmail.com\", role = \"ctb\"),\nperson(\"Borna\", \"Bateni\", email = \"borna@ucla.edu\", role = c(\"aut\", \"cre\")))",
  "Description": "Provides a weighting approach that employs kernels to make\none group have a similar distribution to another group on\ncovariates. This method matches not only means or marginal\ndistributions but also higher-order transformations implied by\nthe choice of kernel. 'kbal' is applicable to both treatment\neffect estimation and survey reweighting problems. Based on\nHazlett, C. (2020) \"Kernel Balancing: A flexible non-parametric\nweighting procedure for estimating causal effects.\" Statistica\nSinica.\n<https://www.researchgate.net/publication/299013953_Kernel_Balancing_A_flexible_non-parametric_weighting_procedure_for_estimating_causal_effects>.",
  "URL": "https://github.com/chadhazlett/KBAL",
  "BugReports": "https://github.com/chadhazlett/KBAL/issues",
  "License": "GPL (>= 2)",
  "Language": "en-US",
  "LazyData": "TRUE",
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  "Date/Publication": "2026-02-28 23:26:24 UTC",
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    "message": "v0.1.5: show (cont.) for continuous outcomes in kpop_summary\n\n- kpop_summary: continuous outcomes now show level = \"(cont.)\" instead\n  of NA, so output tables don't suggest a missing-data problem\n- Bump version to 0.1.5 and update date\n\nCo-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>\n",
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    "getw",
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    "one_hot"
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      "title": "Data from National Supported Work program and Panel Study in Income Dynamics",
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      "class": [
        "data.frame"
      ],
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        "nsw",
        "re78",
        "u78",
        "age",
        "black",
        "hisp",
        "race_ethnicity",
        "married",
        "re74",
        "re75",
        "u74",
        "u75",
        "educ",
        "nodegr"
      ],
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      "table": true,
      "tojson": true
    }
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      ]
    },
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      "title": "Linearized variance for a kpop-weighted mean",
      "topics": [
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      ]
    },
    {
      "page": "calc_ses_cat_var",
      "title": "Weighted proportions and SEs for a categorical outcome",
      "topics": [
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    },
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      "title": "Difference in Means and Difference in Weighted Means",
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      ]
    },
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      "title": "Drop Multicollinear Columns",
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        "drop_multicollin"
      ]
    },
    {
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      "title": "Modified version of ebalance (originally from Jens Hainmueller)",
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      ]
    },
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      "title": "L1 Distance",
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      ]
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      "title": "Find Weights using Entropy Balancing.",
      "topics": [
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      ]
    },
    {
      "page": "kbal",
      "title": "Kernel Balancing",
      "topics": [
        "kbal"
      ]
    },
    {
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      "title": "Kernel balancing for survey reweighting (kpop).",
      "topics": [
        "kpop"
      ]
    },
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      "title": "Build the Gaussian Kernel Matrix",
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      ]
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      "title": "One-Hot Encoding for Categorical Data",
      "topics": [
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      ]
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      "title": "Print method for kpop_summary objects",
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      ]
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