Package: funpca 9.0

funpca: Functional Principal Component Analysis

Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.

Authors:Francisco Rosales [aut, cph, cre], Tatyana Krivobokova [con, ths]

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5 exports 0.00 score 49 dependencies 172 downloads

Last updated 1 years agofrom:92ab27c7fa. Checks:OK: 7. Indexed: yes.

Exports:funpcafunpcaEstplot.funpcaprint.funpcasummary.funpca

Dependencies:ashbitopsBrobdingnagcliclustercolorspacedeSolvefansifarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRCurlrlangscalestibbleutf8vctrsviridisLitewithr