Package: EventStudy
Type: Package
Title: Event Study Analysis
Version: 0.66.0
Date: 2026-10-05
Authors@R: 
    c(person(given = "Simon",
           family = "Mueller",
           role = c("cre", "aut"),
           email = "sm@data-zoo.de"))
Maintainer: Simon Mueller <sm@data-zoo.de>
Description: Financial event study analysis. Implements the classical
        event study methodology of MacKinlay (1997)
        <https://www.jstor.org/stable/2729691> with multiple return
        models: market model, market adjusted, comparison period mean
        adjusted, Fama and French three-factor (1993)
        <doi:10.1016/0304-405X(93)90023-5> and five-factor (2015)
        <doi:10.1016/j.jfineco.2014.10.010>, Carhart (1997) four-factor
        <doi:10.1111/j.1540-6261.1997.tb03808.x>, generalized
        autoregressive conditional heteroskedasticity (GARCH;
        Bollerslev, 1986) <doi:10.1016/0304-4076(86)90063-1>, rolling
        window, and dynamic conditional correlation GARCH (DCC-GARCH;
        Engle, 2002) <doi:10.1198/073500102288618487>. Provides
        parametric and non-parametric test statistics, including the
        standardized cross-sectional test of Boehmer, Musumeci and
        Poulsen (1991) <doi:10.1016/0304-405X(91)90032-F> and its
        cross-correlation adjustment by Kolari and Pynnonen (2010)
        <doi:10.1093/rfs/hhq072>; buy-and-hold abnormal returns (Barber
        and Lyon, 1997) <doi:10.1016/S0304-405X(96)00890-2>; volume and
        volatility event studies; cross-sectional regression; intraday
        event studies; panel difference-in-differences event studies
        with the estimators of Callaway and Sant'Anna (2021)
        <doi:10.1016/j.jeconom.2020.12.001>, de Chaisemartin and
        D'Haultfoeuille (2020) <doi:10.1257/aer.20181169>, and
        Borusyak, Jaravel and Spiess (2024)
        <doi:10.1093/restud/rdae007>; synthetic control methods
        (Abadie, Diamond and Hainmueller, 2010)
        <doi:10.1198/jasa.2009.ap08746>; wild bootstrap inference;
        multiple testing corrections; power analysis via simulation;
        data download helpers; automated reporting; and visualization
        tools. Results can be exported to comma-separated values (CSV),
        'Excel', or 'LaTeX'.
URL: https://github.com/sipemu/eventstudy,
        https://sipemu.github.io/eventstudy/
BugReports: https://github.com/sipemu/eventstudy/issues
License: AGPL-3
Depends: R (>= 4.1.0)
Imports: R6, distributional, methods, plotly, tibble, ggplot2, dplyr,
        purrr, tidyr, stringr, rlang, stats, tools, utils
Suggests: testthat (>= 3.0.0), withr, lifecycle, generics, gridExtra,
        knitr, rmarkdown, openxlsx, rugarch, sandwich, covr, rmgarch,
        quadprog, callr, did, DIDmultiplegt, didimputation, tidyquant,
        quantmod, zoo, httr2 (>= 1.0.0), jsonlite, tinytex, tinytable,
        ragg
Encoding: UTF-8
Config/testthat/edition: 3
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
LazyData: true
NeedsCompilation: no
Packaged: 2026-10-21 12:12:48 UTC; root
Author: Simon Mueller [cre, aut]
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-21 10:19:03 UTC
RemoteUrl: https://github.com/cran/EventStudy
RemoteRef: HEAD
RemoteSha: d538aab6524648a7664b9dca99e9e2876b56604c