The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

cdgd

R-CMD-check CRAN Downloads overall

The package cdgd implements the causal decompositions of group disparities in Yu and Elwert (2023).

Installation

The latest release of the package can be installed through CRAN.

install.packages("cdgd")

The current development version can be installed from source using devtools.

devtools::install_github("ang-yu/cdgd")

Examples

library(cdgd)  

# load the simulated example data
data(exp_data)
head(exp_data)
#>       outcome treatment  confounder          Q group_a
#> 748 1.4608165         1  0.26306864  0.6748330       0
#> 221 0.4777308         0  1.30296394  0.5920512       1
#> 24  0.8760129         1 -1.49971226  1.6294327       1
#> 497 0.4131192         1 -1.17219619 -0.8391873       1
#> 249 2.0483222         1  1.71790879  2.9546966       1
#> 547 0.1912013         0 -0.02438458 -0.3704544       0

Use cdgd0_ml, cdgd0_pa, or cdgd0_manual for unconditional decomposition

results0 <- cdgd0_pa(Y="outcome",D="treatment",G="group_a",X=c("confounder","Q"),data=exp_data,alpha=0.05)

round(results0$results, 4)
#>              point     se p_value CI_lower CI_upper
#> total       0.2675 0.0390  0.0000   0.1911   0.3439
#> baseline    0.0421 0.0131  0.0013   0.0164   0.0678
#> prevalence  0.2579 0.0337  0.0000   0.1919   0.3240
#> effect     -0.1372 0.0209  0.0000  -0.1781  -0.0963
#> selection   0.1047 0.0150  0.0000   0.0754   0.1340

Use cdgd1_ml, cdgd1_pa, or cdgd1_manual for conditional decomposition

results1 <- cdgd1_pa(Y="outcome",D="treatment",G="group_a",X="confounder",Q="Q",data=exp_data,alpha=0.05)

round(results1, 4)
#>                                 point     se p_value CI_lower CI_upper
#> total                          0.2675 0.0390  0.0000   0.1911   0.3439
#> baseline                       0.0421 0.0131  0.0013   0.0164   0.0678
#> conditional prevalence         0.2032 0.0371  0.0000   0.1305   0.2760
#> conditional effect            -0.1644 0.0220  0.0000  -0.2076  -0.1212
#> conditional selection          0.0875 0.0143  0.0000   0.0595   0.1156
#> Q distribution                 0.0990 0.0188  0.0000   0.0621   0.1359
#> conditional Jackson reduction  0.2362 0.0378  0.0000   0.1621   0.3103

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.