Computes a (possibly weighted) dependence measure between `x`

and `y`

if
these are vectors. If `x`

and `y`

are matrices then the measure between the
columns of `x`

and the columns of `y`

are computed.

```
indep_test(
x,
y,
method = "pearson",
weights = NULL,
remove_missing = TRUE,
alternative = "two-sided"
)
```

- x, y
numeric vectors of data values.

`x`

and`y`

must have the same length.- method
the dependence measure; see

*Details*for possible values.- weights
an optional vector of weights for the observations.

- remove_missing
if

`TRUE`

, all (pairswise) incomplete observations are removed; if`FALSE`

, the function throws an error if there are incomplete observations.- alternative
indicates the alternative hypothesis and must be one of

`"two-sided"`

,`"greater"`

or`"less"`

. You can specify just the initial letter.`"greater"`

corresponds to positive association,`"less"`

to negative association.

Available methods:

`"pearson"`

: Pearson correlation`"spearman"`

: Spearman's \(\rho\)`"kendall"`

: Kendall's \(\tau\)`"blomqvist"`

: Blomqvist's \(\beta\)`"hoeffding"`

: Hoeffding's \(D\)

Partial matching of method names is enabled.
All methods except `"hoeffding"`

work with discrete variables.

```
x <- rnorm(100)
y <- rpois(100, 1) # all but Hoeffding's D can handle ties
w <- runif(100)
indep_test(x, y, method = "kendall") # unweighted
#> estimate statistic p_value n_eff method alternative
#> 1 0.07376902 0.8223861 0.4108572 100 kendall two-sided
indep_test(x, y, method = "kendall", weights = w) # weighted
#> estimate statistic p_value n_eff method alternative
#> 1 0.08712423 0.7736649 0.439129 66.66832 kendall two-sided
```