Description Usage Arguments Details Value References See Also Examples

Tests the association between two numeric vectors using Taba robust linear, Taba rank (monotonic), TabWil, or TabWil rank correlation coefficient.

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`x` |
A numeric vector of length greater than 2 must be same length as y |

`y` |
A numeric vector of length greater than 2 must be same length as x |

`method` |
A character string of |

`alternative` |
Character string specifying the alternative hypothesis must be one
of |

`omega` |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |

This function tests the association of two non-empty numeric vectors of
length greater than two, or two columns of a data frame or matrix composed
of more than two numeric elements. Covariates are combined colomn-wise and can be
numeric vectors, matricies, or data frames with numeric cells. Each column in the
matrix or data frame will be treated as a different covariate, and must have
different names. Missing values in either x or y are deleted row-wise. The two sided
test with the null hypothesis correlation is equal to zero. The default is a two
sided test using Taba Linear correlation, with tuning constant `omega`

.

This function returns the robust linear or monotonic association between two numeric vectors, along with it's respective test statistic, and p-value.

The paper is under review for possible publication.

`taba`

for calculating Taba linear or Taba rank (monotonic) correlations
`taba.partial`

for partial and semipartial correlations
`taba.gpartial`

for generalized partial correlations
`taba.matrix`

for calculating correlation, p-value, and distance matricies

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