Description Usage Arguments Details Value Examples

To test pairwise monotonic associations of vectors within one set *m*, run
test_associations(*m*,*m*). Note that the values on the diagonal will not be
necessarily significant if the vectors contain 0's, as it can be seen by the
formula *p_{11}^2 t_{11} + 2 * (p_{00} p_{11} - p_{01} p_{10})*. The formula for the
variance of the estimator proposed by Pimentel(2009) does not apply in case
*p_{11}*, *p_{00}*, *p_{01}*, *p_{10}* attain the values 0 or 1. In these cases the R
function cor.test is used. Note that while independence implies that the
estimator is 0, the estimator being 0 does not imply that the vectors are
independent.

1 2 | ```
test_associations(m1, m2, parallel = FALSE, n_cor = 1,
estimator = "values", d1, d2, p11 = 0, p01 = 0, p10 = 0)
``` |

`m1, m2` |
matrices whose columns are used to estimate the |

`parallel` |
should the computations for combiing the matrices be done in parallel? Default is FALSE. |

`n_cor` |
number of cores to be used if the computation is run in parallel. Default is 1. |

`estimator` |
string indicating how the parameters |

`d1, d2` |
sets of vectors used to estimate |

`p11` |
probability that a bivariate observation is of the type (m,n), where m,n>0 |

`p01` |
probability that a bivariate observation is of the type (0,n), where n>0. |

`p10` |
probability that a bivariate observation is of the type (n,0), where n>0. |

Given two matrices *m_1* and *m_2*, computes all pairwise correlations of each
vector in *m_1* with each vector in *m_2*. Thanks to the package foreach,
computation can be done in parallel using the desired number of cores.

matrix of p-values of association.

1 2 3 4 5 6 7 8 9 10 | ```
v1=c(0,0,10,0,0,12,2,1,0,0,0,0,0,1)
v2=c(0,1,1,0,0,0,1,1,64,3,4,2,32,0)
test_associations(v1,v2)
m1=matrix(c(0,0,10,0,0,12,2,1,0,0,0,0,0,1,1,64,3,4,2,32,0,0,43,54,3,0,0,3,20,1),6)
test_associations(m1,m1)
m2=matrix(c(0,1,1,0,0,0,1,1,64,3,4,2,32,0,0,43,54,3,0,0,3,20,10,0,0,12,2,1,0,0),6)
test_associations(m1,m2)
m3= matrix(abs(rnorm(36)),6)
m4= matrix(abs(rnorm(36)),6)
test_associations(m3,m4)
``` |

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