Science Score: 49.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.4%) to scientific vocabulary
Keywords
Repository
fast and flexible Difference-in-Differences
Basic Info
- Host: GitHub
- Owner: TsaiLintung
- License: other
- Language: R
- Default Branch: main
- Homepage: https://tsailintung.github.io/fastdid
- Size: 36.5 MB
Statistics
- Stars: 29
- Watchers: 3
- Forks: 4
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
fastdid
<!-- badges: start -->
<!-- badges: end -->
fastdid implements the Difference-in-Differences (DiD) estimators in Callaway and Sant'Anna's (2021). fastdid is
- fast, reducing the computation time with millions of units from hours to seconds,
- flexible, allowing extensions such as time-varying covariates Caetano and Callaway, 2024 and multiple events (Tsai 2024).
Getting Started
fastdid can be installed from CRAN,
install.packages("fastdid")
or the latest developmental version can be installed via GitHub,
```
install.packages("devtools")
devtools::install_github("TsaiLintung/fastdid") ```
To use fastdid, you need to provide the dataset data, the column name of time timevar, cohort cohortvar, unit unitvar, and outcome(s) outcomevar. Here is a simple call:
library(fastdid) #loading the package
did_sim <- sim_did(1e+03, 10) #simulate some data
did_estimate <- fastdid(data = did_sim$dt, timevar = "time",
cohortvar = "G", unitvar = "unit", outcomevar = "y")
The function returns a data.table that includes the estimates. Column att is the point estimate, se the standard error of the estimate, att_ciub and att_cilb the confidence interval. The other columns indexes the estimated parameter.
To create event study plots, use plot_did_dynamics(did_estimate).
More
- did: staggered Difference in Difference by Callaway and Sant'Anna
- fastdid: full list of arguments and features.
- double: introduction to DiD with multiple events.
- misc: comparison with did, benchmark, tests, and experimental features.
Acknowledgments
fastdid is created and maintained by Lin-Tung Tsai. Many thanks to Maxwell Kellogg and Kuan-Ju Tseng for their contribution.
Owner
- Login: TsaiLintung
- Kind: user
- Repositories: 1
- Profile: https://github.com/TsaiLintung
GitHub Events
Total
- Issues event: 1
- Watch event: 7
- Delete event: 5
- Issue comment event: 1
- Push event: 23
- Pull request event: 11
- Create event: 4
Last Year
- Issues event: 1
- Watch event: 7
- Delete event: 5
- Issue comment event: 1
- Push event: 23
- Pull request event: 11
- Create event: 4
Packages
- Total packages: 1
-
Total downloads:
- cran 266 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
cran.r-project.org: fastdid
Fast Staggered Difference-in-Difference Estimators
- Homepage: https://github.com/TsaiLintung/fastdid
- Documentation: http://cran.r-project.org/web/packages/fastdid/fastdid.pdf
- License: MIT + file LICENSE
-
Latest release: 1.0.5
published 8 months ago