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  "Title": "Estimating Propensity Scores (PS), PS-Based Weights, and Effects",
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  "Authors@R": "c(\nperson(\"Hanseul\", \"Cho\", email=\"hanseul0618@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Georg\", \"Hahn\", email=\"ghahn@bwh.harvard.edu\", role=\"aut\"),\nperson(\"Janinne\", \"Ortega-Montiel\", email=\"jomontiel@bwh.harvard.edu\", role=\"aut\"),\nperson(\"Julie\", \"Paik\", email=\"jmpaik@mednet.ucla.edu\", role=\"aut\"),\nperson(\"Elisabetta\", \"Patorno\", email=\"epatorno@mednet.ucla.edu\", role=\"aut\")\n)",
  "Description": "Toolbox that provides a streamlined, end-to-end workflow\nfor propensity score analysis in generating real-world evidence\nfrom real-world data. The package covers the full analytic\npipeline - from estimating propensity scores via logistic\nregression, to calculating weights or creating a matched\ncohort, to generating publication-ready Table 1s with\nstandardized mean differences and weighted balance diagnostics.\nIt also estimates incidence rates, hazard ratios, risk ratios,\nand risk differences with support for stratified and\ndirect-standardized analyses. All core functions produce\nformatted 'Excel' reports with embedded 'README' documentation,\nmaking results immediately shareable with collaborators and\nstakeholders. Methods are based on Rosenbaum and Rubin (1983)\n<doi:10.1093/biomet/70.1.41>, Austin (2011)\n<doi:10.1080/00273171.2011.568786>, and Desai et al. (2017)\n<doi:10.1097/EDE.0000000000000595>.",
  "License": "MIT + file LICENSE",
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  "URL": "https://github.com/hanseul0618/rwetools",
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    "User": "root"
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  "Author": "Hanseul Cho [aut, cre], Georg Hahn [aut], Janinne\nOrtega-Montiel [aut], Julie Paik [aut], Elisabetta Patorno\n[aut]",
  "Maintainer": "Hanseul Cho <hanseul0618@gmail.com>",
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