Multivariate Distance Correlation for two sets of variables

Computes the distances within two sets of variables and the corresponding distance correlation.

1 | ```
dcorMVtable(x, ind = 1, method = "euclidean")
``` |

`x` |
A contingency table of class |

`ind` |
The indices for the first set of variables. The second set consists of all remaining variables. |

`method` |
The method for dist |

The distance correlation between 0 and 1 for the distances from the two sets of variables.

This code has not been tested thoroughly and may still contain errors.

Alexander Pilhoefer

dcorMVdata, wdcor, approx.dcor

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Not run:
A2 <- arsim(2000,c(8,9),5,0.1)
A2 <- optile(A2, iter=100)
BCI(A2)
wdcor(A2)
p1 <- runif(11)+0.1
p1 <- p1/sum(p1)
A2b <- apply(A2,1:2,function(z) rmultinom(1,z,p1))
# now the first variable is roughly independent from the other two:
dcorMVtable(as.table(A2b),ind = 1)
# here the third variable is NOT independent from the others:
A3 <- arsim(2000,c(8,9,11),5,0.1)
A3 <- optile(A3, iter=100)
BCI(A3)
dcorMVtable(A3,ind = 3)
## End(Not run)
``` |

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