Package: fdaACF 1.0.0

Guillermo Mestre Marcos

fdaACF: Autocorrelation Function for Functional Time Series

Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.

Authors:Guillermo Mestre Marcos [aut, cre], José Portela González [aut], Gregory Rice [aut], Antonio Muñoz San Roque [ctb], Estrella Alonso Pérez [ctb]

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Peer review:

Bug tracker:https://github.com/gmestrem/fdaacf/issues

On CRAN:

17 exports 8 stars 1.33 score 55 dependencies 11 scripts 209 downloads

Last updated 4 years agofrom:d660e57355. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:estimate_iid_distr_Imhofestimate_iid_distr_MCfit_ARHp_FPCAFTS_identificationintegral_operatormat2fdobtain_autocorrelationobtain_autocov_eigenvaluesobtain_autocovarianceobtain_FACFobtain_FPACFobtain_suface_L2_normplot_autocovarianceplot_FACFreconstruct_fd_from_PCAsimulate_iid_brownian_bridgesimulate_iid_brownian_motion

Dependencies:ashbitopscliclustercolorspaceCompQuadFormdeSolvefansifarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelmtestlocfitmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRCurlrlangsandwichscalesstrucchangetibbleurcautf8varsvctrsviridisLitewithrzoo