The package weightedGCM contains two functions implementing the two versions of the Weighted Generalised Covariance Measure (WGCM) conditional independence test described in Scheidegger, Hoerrmann and Buehlmann (2021) “The Weighted Generalised Covariance Measure” <arXiv:2111.04361>. It is a generalisation of the Generalised Covariance Measure (GCM) implemented in the package ‘GeneralisedCovarianceMeasure’ by Jonas Peters and Rajen D. Shah based on Shah and Peters (2020) “The Hardness of Conditional Independence Testing and the Generalised Covariance Measure” <arXiv:1804.07203>.

*wgcm.fix*calculates a p-value for the null hypothesis of conditional independence based on the WGCM using several fixed weight functions.*wgcm.est*calculates a p-value for the null hypothesis of conditional indepencence based on the WGCM using a single estimated weight function.

You can install the released version of weightedGCM from CRAN with:

We generate some data to use the two conditional independence test.

```
library(weightedGCM)
## Generate data
set.seed(1)
n <- 200
Z <- rnorm(n)
X <- Z + 0.3*rnorm(n)
## Y1 _||_ X | Z
Y1 <- Z + 0.3*rnorm(n)
## Y2 not _||_ X | Z
Y2 <- Z + 0.3*rnorm(n) + 0.3*X
## Y3 not _||_ X | Z
Y3 <- Z + 0.3*rnorm(n) + 0.15*X^2
## Test for conditional independence using wgcm.fix()
wgcm.fix(X, Y1, Z, regr.meth = "gam", weight.num = 7, weight.meth = "sign")
#> [1] 0.868
wgcm.fix(X, Y2, Z, regr.meth = "gam", weight.num = 7, weight.meth = "sign")
#> [1] 0.004
wgcm.fix(X, Y3, Z, regr.meth = "gam", weight.num = 7, weight.meth = "sign")
#> [1] 0.004
## Test for conditional independence using wgcm.est()
wgcm.est(X, Y1, Z, beta = 0.3, regr.meth = "gam")
#> [1] 0.2389116
wgcm.est(X, Y2, Z, beta = 0.3, regr.meth = "gam")
#> [1] 9.974068e-05
wgcm.est(X, Y3, Z, beta = 0.3, regr.meth = "gam")
#> [1] 0.004474768
```