{
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  "Package": "nonabsdid",
  "Type": "Package",
  "Title": "Visualize Heterogeneity-Robust Event Studies for Non-Absorbing\nTreatments",
  "Version": "0.4.1.9000",
  "Date": "2026-06-23",
  "Authors@R": "c(\nperson(given = \"Takuma\",\nfamily = \"Iwasaki\",\nrole = c(\"aut\", \"cre\"),\nemail = \"iwasakit@stanford.edu\",\ncomment = c(ORCID = \"0009-0000-8782-4851\"))\n)",
  "Description": "Runs several heterogeneity-robust\ndifference-in-differences (DID) event-study estimators for\nnon-absorbing (i.e., treatment can switch on and off over time,\nallowing treatment reversal) binary treatments through their\nrespective packages, harmonizes their output onto a common time\naxis and tidy data structure, and overlays them in a single\n'ggplot2' panel for visual comparison. Supported estimators\ninclude those provided by 'DIDmultiplegtDYN', 'PanelMatch', and\n'fect', with an optional naive two-way fixed-effects reference\nseries via 'fixest'. The underlying methods are respectively\ndescribed in Clement de Chaisemartin and Xavier\nD'Haultfoeuille. \"Difference-in-Differences Estimators of\nIntertemporal Treatment Effects.\" The Review of Economics and\nStatistics (2026) <doi:10.1162/rest_a_01414>, Kosuke Imai, In\nSong Kim, and Erik H. Wang. \"Matching methods for causal\ninference with time‐series cross‐sectional data.\" American\nJournal of Political Science 67.3 (2023)\n<doi:10.1111/ajps.12685>, Licheng Liu, Ye Wang, and Yiqing Xu.\n\"A practical guide to counterfactual estimators for causal\ninference with time‐series cross‐sectional data.\" American\nJournal of Political Science 68.1 (2024)\n<doi:10.1111/ajps.12723>, and Laurent R. Bergé, Kyle Butts, and\nGrant McDermott. \"Fast and user-friendly econometrics\nestimations: The R package 'fixest'.\" arXiv preprint (2026)\n<doi:10.48550/arXiv.2601.21749>. A single nabs_event_study()\nwrapper runs any supported estimator with a common interface;\nnabs_event_study_simple() provides a one-line front door for\nquick exploratory runs; the S3 generic as_nabs_event_study()\ncoerces estimator output into a tidy stable schema; and\nnabs_event_plot() overlays multiple methods on a single\n'ggplot2' panel, with optional naive two-way fixed effects\ndrawn in a neutral color as a reference.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Config/testthat/edition": "3",
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  "Date/Publication": "2026-07-07 21:23:51 UTC",
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  "Author": "Takuma Iwasaki [aut, cre] (ORCID:\n<https://orcid.org/0009-0000-8782-4851>)",
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    "as_nabs_event_study",
    "nabs_effect_cells",
    "nabs_event_plot",
    "nabs_event_study",
    "nabs_event_study_simple",
    "nabs_read_dta",
    "nabs_write_dta",
    "naive_twfe",
    "plot_effect_matrix"
  ],
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    {
      "page": "aggregate_effects",
      "title": "Collapse effect cells back onto an event-study path",
      "topics": [
        "aggregate_effects"
      ]
    },
    {
      "page": "as_nabs_effect_cells",
      "title": "Coerce an estimator result to a tidy cohort-by-time effect-cell tibble",
      "topics": [
        "as_nabs_effect_cells",
        "as_nabs_effect_cells.data.frame",
        "as_nabs_effect_cells.did_multiplegt_dyn",
        "as_nabs_effect_cells.fect"
      ]
    },
    {
      "page": "as_nabs_event_study",
      "title": "Coerce an estimator result to a tidy event-study tibble",
      "topics": [
        "as_nabs_event_study",
        "as_nabs_event_study.did_multiplegt_dyn",
        "as_nabs_event_study.fect",
        "as_nabs_event_study.fixest",
        "as_nabs_event_study.list",
        "as_nabs_event_study.nabs_event_study_result",
        "as_nabs_event_study.nabs_event_study_simple",
        "as_nabs_event_study.PanelEstimate"
      ]
    },
    {
      "page": "nabs_effect_cells",
      "title": "Fit an estimator and return cohort-by-time effect cells",
      "topics": [
        "nabs_effect_cells"
      ]
    },
    {
      "page": "nabs_event_plot",
      "title": "Plot one or more event-study tibbles on a single panel",
      "topics": [
        "nabs_event_plot"
      ]
    },
    {
      "page": "nabs_event_study",
      "title": "Run an event-study estimator with a unified interface",
      "topics": [
        "nabs_event_study"
      ]
    },
    {
      "page": "nabs_event_study_simple",
      "title": "One-line exploratory front door for non-absorbing event studies",
      "topics": [
        "nabs_event_study_simple"
      ]
    },
    {
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      "title": "Read a Stata .dta file into an analysis-ready data frame",
      "topics": [
        "nabs_read_dta"
      ]
    },
    {
      "page": "nabs_write_dta",
      "title": "Write event-study results to a Stata .dta file",
      "topics": [
        "nabs_write_dta"
      ]
    },
    {
      "page": "naive_twfe",
      "title": "Estimate a naive two-way fixed-effects (TWFE) event study",
      "topics": [
        "naive_twfe"
      ]
    },
    {
      "page": "plot_effect_matrix",
      "title": "Plot a cohort-by-time effect matrix as a heatmap",
      "topics": [
        "plot_effect_matrix"
      ]
    }
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        "1. Why use R for this at all?",
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        "3. Rosetta stone: did_multiplegt_dyn → nabs_event_study()",
        "What about csdid / did_imputation / xtevent?",
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