`ci.test(x, y, z, data, test, B, debug = FALSE)`

x

a character string (the name of a variable), a data frame, a numeric
vector or a factor object.

y

a character string (the name of another variable), a numeric vector
or a factor object.

z

a vector of character strings (the names of the conditioning
variables), a numeric vector, a factor object or a data frame. If

`NULL`

an independence test will be executed.data

a data frame containing the variables to be tested.

test

a character string, the label of the conditional independence
test to be used in the algorithm. If none is specified, the default test
statistic is the *mutual information* for categorical variables, the
Jonckheere-Terpstra test for ordered factors and the *linear
correlation* for continuous variables. See

`bnlearn-package`

for details.B

a positive integer, the number of permutations considered for each
permutation test. It will be ignored with a warning if the conditional
independence test specified by the

`test`

argument is not a
permutation test.debug

a boolean value. If

`TRUE`

a lot of debugging output is
printed; otherwise the function is completely silent.`htest`

containing the following components: `choose.direction`

, `arc.strength`

.```
data(gaussian.test)
data(learning.test)
# using a data frame and column labels.
ci.test(x = "F" , y = "B", z = c("C", "D"), data = gaussian.test)
# using a data frame.
ci.test(gaussian.test)
# using factor objects.
attach(learning.test)
ci.test(x = F , y = B, z = data.frame(C, D))
```

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