This function computes the matrix of partial correlations via an estimation of the corresponding regression models via Partial Least Squares.

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`X` |
matrix of observations. The rows of |

`scale` |
Scale the columns of X? Default is scale=TRUE. |

`k` |
Number of splits in |

`ncomp` |
Maximal number of components. Default is 15. |

`verbose` |
Print information on conflicting signs etc. Default is |

For each of the columns of `X`

, a regression model based on
Partial Least Squares is computed. The optimal model is determined via
cross-validation. The results of the regression models are
transformed via the function `Beta2parcor`

.

`pcor` |
estimated matrix of partial correlation coefficients. |

`m` |
optimal number of components for each of the |

Nicole Kraemer

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384

http://www.biomedcentral.com/1471-2105/10/384/

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