Nothing
README.md
In staggR: Fit Difference-in-Differences Models with Staggered Interventions
staggR
staggR makes it easy to fit linear difference-in-differences models
where interventions are staggered over time across multiple cohorts. The
approach uses cohort- and time-since-treatment specific
difference-in-differences parameters, and it provides convenience
functions both for specifying the model and for flexibly aggregating
coefficients to answer a variety of research questions.
Installation
install.packages("staggR")
Example
This is a basic example which shows you how to solve a common problem:
library(staggR)
#' Fit the staggered DiD model
sdid_hosp <- staggR::sdid(hospitalized ~ cohort + yr,
df = hosp,
intervention_var = "intervention_yr",
.vcov = sandwich::vcovCL,
cluster = hosp$county)
summary(sdid_hosp)
#>
#> Supplied formula:
#> hospitalized ~ cohort + yr
#>
#> Fitted formula:
#> hospitalized ~ cohort_5 + cohort_6 + cohort_7 + cohort_8 + yr_2011 +
#> yr_2012 + yr_2013 + yr_2014 + yr_2015 + yr_2016 + yr_2017 +
#> yr_2018 + yr_2019 + yr_2020 + cohort_5:yr_2010 + cohort_5:yr_2011 +
#> cohort_5:yr_2012 + cohort_5:yr_2013 + cohort_5:yr_2015 +
#> cohort_5:yr_2016 + cohort_5:yr_2017 + cohort_5:yr_2018 +
#> cohort_5:yr_2019 + cohort_5:yr_2020 + cohort_6:yr_2010 +
#> cohort_6:yr_2011 + cohort_6:yr_2012 + cohort_6:yr_2013 +
#> cohort_6:yr_2014 + cohort_6:yr_2016 + cohort_6:yr_2017 +
#> cohort_6:yr_2018 + cohort_6:yr_2019 + cohort_6:yr_2020 +
#> cohort_7:yr_2010 + cohort_7:yr_2011 + cohort_7:yr_2012 +
#> cohort_7:yr_2013 + cohort_7:yr_2014 + cohort_7:yr_2015 +
#> cohort_7:yr_2017 + cohort_7:yr_2018 + cohort_7:yr_2019 +
#> cohort_7:yr_2020 + cohort_8:yr_2010 + cohort_8:yr_2011 +
#> cohort_8:yr_2012 + cohort_8:yr_2013 + cohort_8:yr_2014 +
#> cohort_8:yr_2015 + cohort_8:yr_2016 + cohort_8:yr_2018 +
#> cohort_8:yr_2019 + cohort_8:yr_2020
#>
#> Residuals:
#> Min Q1 Median Q3 Max
#> -0.8007 -0.3788 -0.2139 0.4595 0.84
#>
#> Coefficients:
#> term estimate std_error t_value p_value
#> (Intercept) 0.228155340 0.031307912 7.2874659 3.2e-13 ***
#> cohort_5 0.088156223 0.028579679 3.0845771 0.00204 **
#> cohort_6 -0.159451272 0.016375109 -9.7374175 0.0e+00 ***
#> cohort_7 0.075133698 0.018413395 4.0803827 0.00005 ***
#> cohort_8 -0.237490795 0.003992926 -59.4778926 0.0e+00 ***
#> yr_2011 0.055426750 0.027992651 1.9800465 0.04771 *
#> yr_2012 0.025200365 0.048720589 0.5172426 0.60499
#> yr_2013 0.047663300 0.034037028 1.4003367 0.16142
#> yr_2014 0.110034046 0.035770010 3.0761536 0.00210 **
#> yr_2015 0.130370006 0.039124167 3.3322116 0.00086 ***
#> yr_2016 0.150645703 0.037030770 4.0681224 0.00005 ***
#> yr_2017 0.193950839 0.030086602 6.4464189 1.2e-10 ***
#> yr_2018 0.198984915 0.042004187 4.7372638 2.2e-06 ***
#> yr_2019 0.239084188 0.032639986 7.3248863 2.4e-13 ***
#> yr_2020 0.226537217 0.032775690 6.9117451 4.9e-12 ***
#> cohort_5:yr_2010 -0.075771022 0.048141117 -1.5739357 0.11551
#> cohort_5:yr_2011 -0.073492699 0.044324381 -1.6580649 0.09731 .
#> cohort_5:yr_2012 0.008214848 0.029858839 0.2751228 0.78322
#> cohort_5:yr_2013 0.018970874 0.035225443 0.5385560 0.59020
#> cohort_5:yr_2015 0.031183015 0.047308110 0.6591473 0.50981
#> cohort_5:yr_2016 0.073549064 0.039113486 1.8804016 0.06006 .
#> cohort_5:yr_2017 0.111184626 0.039801432 2.7934831 0.00522 **
#> cohort_5:yr_2018 0.181886621 0.033410927 5.4439263 5.3e-08 ***
#> cohort_5:yr_2019 0.188240613 0.019982991 9.4200420 0.0e+00 ***
#> cohort_5:yr_2020 0.257882482 0.043630603 5.9105871 3.4e-09 ***
#> cohort_6:yr_2010 0.192165497 0.040766852 4.7137683 2.4e-06 ***
#> cohort_6:yr_2011 0.083833784 0.021768788 3.8511001 0.00012 ***
#> cohort_6:yr_2012 0.130360273 0.017418812 7.4838784 7.4e-14 ***
#> cohort_6:yr_2013 0.103217201 0.054669436 1.8880239 0.05903 .
#> cohort_6:yr_2014 0.019231429 0.018934185 1.0156988 0.30978
#> cohort_6:yr_2016 0.039597597 0.014527663 2.7256687 0.00642 **
#> cohort_6:yr_2017 -0.017621795 0.045720916 -0.3854209 0.69993
#> cohort_6:yr_2018 -0.065811049 0.024388171 -2.6984823 0.00697 **
#> cohort_6:yr_2019 -0.071375212 0.028617604 -2.4941016 0.01263 *
#> cohort_6:yr_2020 -0.083590800 0.043178676 -1.9359278 0.05289 .
#> cohort_7:yr_2010 -0.028841089 0.040446429 -0.7130688 0.47581
#> cohort_7:yr_2011 -0.097944813 0.025984565 -3.7693458 0.00016 ***
#> cohort_7:yr_2012 -0.046622258 0.030682909 -1.5194862 0.12865
#> cohort_7:yr_2013 -0.036083533 0.037344341 -0.9662383 0.33393
#> cohort_7:yr_2014 -0.053870348 0.028528256 -1.8883155 0.05899 .
#> cohort_7:yr_2015 -0.042354696 0.030159042 -1.4043780 0.16022
#> cohort_7:yr_2017 0.123013287 0.013601784 9.0439086 0.0e+00 ***
#> cohort_7:yr_2018 0.131938968 0.032104648 4.1096531 0.00004 ***
#> cohort_7:yr_2019 0.159289976 0.015694481 10.1494260 0.0e+00 ***
#> cohort_7:yr_2020 0.189118922 0.037494013 5.0439765 4.6e-07 ***
#> cohort_8:yr_2010 0.169335455 0.030086602 5.6282679 1.8e-08 ***
#> cohort_8:yr_2011 0.162863929 0.014472854 11.2530626 0.0e+00 ***
#> cohort_8:yr_2012 0.231193914 0.024009151 9.6294083 0.0e+00 ***
#> cohort_8:yr_2013 0.185971220 0.018317313 10.1527563 0.0e+00 ***
#> cohort_8:yr_2014 0.166476981 0.011655637 14.2829587 0.0e+00 ***
#> cohort_8:yr_2015 0.050939971 0.009977242 5.1056167 3.3e-07 ***
#> cohort_8:yr_2016 0.059839177 0.007804591 7.6671765 1.8e-14 ***
#> cohort_8:yr_2018 0.009379666 0.013978605 0.6710016 0.50222
#> cohort_8:yr_2019 0.036917934 0.003425534 10.7772793 0.0e+00 ***
#> cohort_8:yr_2020 -0.003312873 0.016633407 -0.1991698 0.84213
#>
#> Significance codes: < 0.001: '**'; < 0.01: '**'; < 0.05: '*'; < 0.1: '.'
#> Residual standard error: 0.4632 on 30985 degrees of freedom
#> R^2: 0.118875596336873; Adjusted R^2: 0.117339991437799
#' What is the effect of the intervention in the post-intervention period?
ave_coeff(sdid = sdid_hosp,
coefs = select_period(sdid_hosp, period = "post"))
#> est se pval sign lb ub n
#> 1 0.09962262 0.0159212 3.890523e-10 *** 0.06841706 0.1308282 11745
Getting help
If you encounter a problem, please file an issue with a minimal
reproducible example on GitHub.
Try the staggR package in your browser
Any scripts or data that you put into this service are public.
staggR documentation built on April 2, 2026, 5:08 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
staggR
staggR makes it easy to fit linear difference-in-differences models where interventions are staggered over time across multiple cohorts. The approach uses cohort- and time-since-treatment specific difference-in-differences parameters, and it provides convenience functions both for specifying the model and for flexibly aggregating coefficients to answer a variety of research questions.
Installation
install.packages("staggR")
Example
This is a basic example which shows you how to solve a common problem:
library(staggR)
#' Fit the staggered DiD model
sdid_hosp <- staggR::sdid(hospitalized ~ cohort + yr,
df = hosp,
intervention_var = "intervention_yr",
.vcov = sandwich::vcovCL,
cluster = hosp$county)
summary(sdid_hosp)
#>
#> Supplied formula:
#> hospitalized ~ cohort + yr
#>
#> Fitted formula:
#> hospitalized ~ cohort_5 + cohort_6 + cohort_7 + cohort_8 + yr_2011 +
#> yr_2012 + yr_2013 + yr_2014 + yr_2015 + yr_2016 + yr_2017 +
#> yr_2018 + yr_2019 + yr_2020 + cohort_5:yr_2010 + cohort_5:yr_2011 +
#> cohort_5:yr_2012 + cohort_5:yr_2013 + cohort_5:yr_2015 +
#> cohort_5:yr_2016 + cohort_5:yr_2017 + cohort_5:yr_2018 +
#> cohort_5:yr_2019 + cohort_5:yr_2020 + cohort_6:yr_2010 +
#> cohort_6:yr_2011 + cohort_6:yr_2012 + cohort_6:yr_2013 +
#> cohort_6:yr_2014 + cohort_6:yr_2016 + cohort_6:yr_2017 +
#> cohort_6:yr_2018 + cohort_6:yr_2019 + cohort_6:yr_2020 +
#> cohort_7:yr_2010 + cohort_7:yr_2011 + cohort_7:yr_2012 +
#> cohort_7:yr_2013 + cohort_7:yr_2014 + cohort_7:yr_2015 +
#> cohort_7:yr_2017 + cohort_7:yr_2018 + cohort_7:yr_2019 +
#> cohort_7:yr_2020 + cohort_8:yr_2010 + cohort_8:yr_2011 +
#> cohort_8:yr_2012 + cohort_8:yr_2013 + cohort_8:yr_2014 +
#> cohort_8:yr_2015 + cohort_8:yr_2016 + cohort_8:yr_2018 +
#> cohort_8:yr_2019 + cohort_8:yr_2020
#>
#> Residuals:
#> Min Q1 Median Q3 Max
#> -0.8007 -0.3788 -0.2139 0.4595 0.84
#>
#> Coefficients:
#> term estimate std_error t_value p_value
#> (Intercept) 0.228155340 0.031307912 7.2874659 3.2e-13 ***
#> cohort_5 0.088156223 0.028579679 3.0845771 0.00204 **
#> cohort_6 -0.159451272 0.016375109 -9.7374175 0.0e+00 ***
#> cohort_7 0.075133698 0.018413395 4.0803827 0.00005 ***
#> cohort_8 -0.237490795 0.003992926 -59.4778926 0.0e+00 ***
#> yr_2011 0.055426750 0.027992651 1.9800465 0.04771 *
#> yr_2012 0.025200365 0.048720589 0.5172426 0.60499
#> yr_2013 0.047663300 0.034037028 1.4003367 0.16142
#> yr_2014 0.110034046 0.035770010 3.0761536 0.00210 **
#> yr_2015 0.130370006 0.039124167 3.3322116 0.00086 ***
#> yr_2016 0.150645703 0.037030770 4.0681224 0.00005 ***
#> yr_2017 0.193950839 0.030086602 6.4464189 1.2e-10 ***
#> yr_2018 0.198984915 0.042004187 4.7372638 2.2e-06 ***
#> yr_2019 0.239084188 0.032639986 7.3248863 2.4e-13 ***
#> yr_2020 0.226537217 0.032775690 6.9117451 4.9e-12 ***
#> cohort_5:yr_2010 -0.075771022 0.048141117 -1.5739357 0.11551
#> cohort_5:yr_2011 -0.073492699 0.044324381 -1.6580649 0.09731 .
#> cohort_5:yr_2012 0.008214848 0.029858839 0.2751228 0.78322
#> cohort_5:yr_2013 0.018970874 0.035225443 0.5385560 0.59020
#> cohort_5:yr_2015 0.031183015 0.047308110 0.6591473 0.50981
#> cohort_5:yr_2016 0.073549064 0.039113486 1.8804016 0.06006 .
#> cohort_5:yr_2017 0.111184626 0.039801432 2.7934831 0.00522 **
#> cohort_5:yr_2018 0.181886621 0.033410927 5.4439263 5.3e-08 ***
#> cohort_5:yr_2019 0.188240613 0.019982991 9.4200420 0.0e+00 ***
#> cohort_5:yr_2020 0.257882482 0.043630603 5.9105871 3.4e-09 ***
#> cohort_6:yr_2010 0.192165497 0.040766852 4.7137683 2.4e-06 ***
#> cohort_6:yr_2011 0.083833784 0.021768788 3.8511001 0.00012 ***
#> cohort_6:yr_2012 0.130360273 0.017418812 7.4838784 7.4e-14 ***
#> cohort_6:yr_2013 0.103217201 0.054669436 1.8880239 0.05903 .
#> cohort_6:yr_2014 0.019231429 0.018934185 1.0156988 0.30978
#> cohort_6:yr_2016 0.039597597 0.014527663 2.7256687 0.00642 **
#> cohort_6:yr_2017 -0.017621795 0.045720916 -0.3854209 0.69993
#> cohort_6:yr_2018 -0.065811049 0.024388171 -2.6984823 0.00697 **
#> cohort_6:yr_2019 -0.071375212 0.028617604 -2.4941016 0.01263 *
#> cohort_6:yr_2020 -0.083590800 0.043178676 -1.9359278 0.05289 .
#> cohort_7:yr_2010 -0.028841089 0.040446429 -0.7130688 0.47581
#> cohort_7:yr_2011 -0.097944813 0.025984565 -3.7693458 0.00016 ***
#> cohort_7:yr_2012 -0.046622258 0.030682909 -1.5194862 0.12865
#> cohort_7:yr_2013 -0.036083533 0.037344341 -0.9662383 0.33393
#> cohort_7:yr_2014 -0.053870348 0.028528256 -1.8883155 0.05899 .
#> cohort_7:yr_2015 -0.042354696 0.030159042 -1.4043780 0.16022
#> cohort_7:yr_2017 0.123013287 0.013601784 9.0439086 0.0e+00 ***
#> cohort_7:yr_2018 0.131938968 0.032104648 4.1096531 0.00004 ***
#> cohort_7:yr_2019 0.159289976 0.015694481 10.1494260 0.0e+00 ***
#> cohort_7:yr_2020 0.189118922 0.037494013 5.0439765 4.6e-07 ***
#> cohort_8:yr_2010 0.169335455 0.030086602 5.6282679 1.8e-08 ***
#> cohort_8:yr_2011 0.162863929 0.014472854 11.2530626 0.0e+00 ***
#> cohort_8:yr_2012 0.231193914 0.024009151 9.6294083 0.0e+00 ***
#> cohort_8:yr_2013 0.185971220 0.018317313 10.1527563 0.0e+00 ***
#> cohort_8:yr_2014 0.166476981 0.011655637 14.2829587 0.0e+00 ***
#> cohort_8:yr_2015 0.050939971 0.009977242 5.1056167 3.3e-07 ***
#> cohort_8:yr_2016 0.059839177 0.007804591 7.6671765 1.8e-14 ***
#> cohort_8:yr_2018 0.009379666 0.013978605 0.6710016 0.50222
#> cohort_8:yr_2019 0.036917934 0.003425534 10.7772793 0.0e+00 ***
#> cohort_8:yr_2020 -0.003312873 0.016633407 -0.1991698 0.84213
#>
#> Significance codes: < 0.001: '**'; < 0.01: '**'; < 0.05: '*'; < 0.1: '.'
#> Residual standard error: 0.4632 on 30985 degrees of freedom
#> R^2: 0.118875596336873; Adjusted R^2: 0.117339991437799
#' What is the effect of the intervention in the post-intervention period?
ave_coeff(sdid = sdid_hosp,
coefs = select_period(sdid_hosp, period = "post"))
#> est se pval sign lb ub n
#> 1 0.09962262 0.0159212 3.890523e-10 *** 0.06841706 0.1308282 11745
Getting help
If you encounter a problem, please file an issue with a minimal reproducible example on GitHub.
Try the staggR package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.