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NonlinearDiD  

Staggered Difference-in-Differences with Nonlinear Outcomes
View on CRAN: Click here


Download and install NonlinearDiD package within the R console
Install from CRAN:
install.packages("NonlinearDiD")

Install from Github:
library("remotes")
install_github("cran/NonlinearDiD")

Install by package version:
library("remotes")
install_version("NonlinearDiD", "0.2.0")



Attach the package and use:
library("NonlinearDiD")
Maintained by
Subir Hait
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-05
Latest Update: 2026-05-05
Description:
Implements difference-in-differences estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes. Extends Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001> to handle the fundamental identification challenges that arise with nonlinear outcome models (logit, probit, Poisson) in heterogeneous treatment timing designs. Provides group-time average treatment effects on the treated (ATT), aggregation schemes, and pre-treatment parallel trends tests appropriate for nonlinear settings. Methods include doubly-robust semiparametric estimators, nonparametric bounds, and an odds-ratio DiD approach for binary outcomes. Methods extend Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>, Roth and Sant'Anna (2023) <doi:10.3982/ECTA19255>, and Wooldridge (2023) <doi:10.1093/ectj/utad016>.
How to cite:
Subir Hait (2026). NonlinearDiD: Staggered Difference-in-Differences with Nonlinear Outcomes. R package version 0.2.0, https://cran.r-project.org/web/packages/NonlinearDiD. Accessed 24 Oct. 2026.
Previous versions and publish date:
0.1.0 (2026-05-05 21:30), (2026-07-09 08:15)
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Complete documentation for NonlinearDiD
Functions, R codes and Examples using the NonlinearDiD R package
Full NonlinearDiD package functions and examples
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