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"
)

## Arguments

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. |

## Details

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.

## Examples

#> estimate statistic p_value n_eff method alternative
#> 1 -0.01762804 -0.2129434 0.8313711 100 kendall two-sided

indep_test(x, y, method = "kendall", weights = w) # weighted

#> estimate statistic p_value n_eff method alternative
#> 1 0.01242062 0.1281651 0.8980183 74.08639 kendall two-sided