This function will run a weighted functional pca in the two cases of uni, and multivariate cases. If the observations (the curves) are given with weights, set up the parameter tik.

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`fd` |
in the univariate case fd is an object from a class fd. Otherwise in the multivariate case fd is a list of fd object (fd=list(fd1,fd2,..)). |

`nharm` |
number of harmonics or principal component to be retain. |

`tik` |
the weights of the functional pca which corresponds to the weights of the curves. If don't given, then we will run a classic functional pca (without weighting the curves). |

When univarite functional data, the function are returning an object of calss "pca.fd", When multivariate a list of "pca.fd" object by dimension. The "pca.fd" class contains the folowing parameter: harmonics: functional data object storing the eigen function values: the eigenvalues varprop: the normalized eigenvalues (eigenvalues divide by their sum) scores: the scores matrix meanfd: the mean of the functional data object

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