fda (R)

fda: Functional Data Analysis , These functions were developed to support functional data analysis as described in Ramsay, J. O. and Silverman, B. W. (2005) Functional Data Analysis. New York: Springer. They were ported from earlier versions in Matlab and S-PLUS. An introduction appears in Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009) Functional Data Analysis with R and Matlab (Springer). The package includes data sets and script files working many examples including all but one of the 76 figures in this latter book. As of this release, the R-Project is no longer distributing the Matlab versions of the functional data analysis functions and sample analyses through the CRAN distribution system. This is due to the pressure placed on storage required in the many CRAN sites by the rapidly increasing number of R packages, of which the fda package is one. The three of us involved in this package have agreed to help out this situation by switching to distributing the Matlab functions and analyses through Jim Ramsay’s ftp site at McGill University. To obtain these Matlab files, go to this site using an ftp utility: http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/ There you find a set of .zip files containing the functions and sample analyses, as well as two .txt files giving instructions for installation and some additional information. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 1588 articles , 1 standard article )

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  1. Almanjahie, Ibrahim M.; Aissiri, Khlood A.; Laksaci, Ali; Elmezouar, Zouaoui Chikr: The (k) nearest neighbors smoothing of the relative-error regression with functional regressor (2022)
  2. Almanjahie, Ibrahim M.; Bouzebda, Salim; Chikr Elmezouar, Zouaoui; Laksaci, Ali: The functional (k\mathrmNN) estimator of the conditional expectile: uniform consistency in number of neighbors (2022)
  3. Alodat, Tareq; Olenko, Andriy: On asymptotics of discretized functionals of long-range dependent functional data (2022)
  4. Álvarez-Esteban, Pedro C.; García-Escudero, Luis A.: Robust clustering of functional directional data (2022)
  5. Amel, Azzi; Ali, Laksaci; Elias, Ould Saïd: On the robustification of the kernel estimator of the functional modal regression (2022)
  6. Aneiros, Germán; Horová, Ivana; Hušková, Marie; Vieu, Philippe: On functional data analysis and related topics (2022)
  7. Aneiros, Germán; Novo, Silvia; Vieu, Philippe: Variable selection in functional regression models: a review (2022)
  8. Aubin, Jean-Baptiste; Bongiorno, Enea G.; Goia, Aldo: The correction term in a small-ball probability factorization for random curves (2022)
  9. Baek, Seungchul; Kim, Yewon; Park, Junyong; Lee, Jong Soo: Revisit to functional data analysis of sleeping energy expenditure (2022)
  10. Balogoun, Armando S. K.; Nkiet, Guy M.; Ogouyandjou, Carlos: Kernel based method for the (k)-sample problem with functional data (2022)
  11. Benth, Fred Espen; Schroers, Dennis; Veraart, Almut E. D.: A weak law of large numbers for realised covariation in a Hilbert space setting (2022)
  12. Beyaztas, Ufuk; Shang, Han Lin: Function-on-function linear quantile regression (2022)
  13. Beyaztas, Ufuk; Shang, Han Lin: A robust partial least squares approach for function-on-function regression (2022)
  14. Beyaztas, Ufuk; Shang, Han Lin; Alin, Aylin: Function-on-function partial quantile regression (2022)
  15. Bhattacharjee, Satarupa; Müller, Hans-Georg: Concurrent object regression (2022)
  16. Bingham, N. H.: Prediction theory for stationary functional time series (2022)
  17. Birbilas, Algimantas; Račkauskas, Alfredas: Functional modelling of telecommunications data (2022)
  18. Boukhiar, Souad; Mourid, Tahar: Resolvent estimators for functional autoregressive processes with random coefficients (2022)
  19. Bouzebda, Salim; Chaouch, Mohamed: Uniform limit theorems for a class of conditional (Z)-estimators when covariates are functions (2022)
  20. Bouzebda, Salim; Didi, Sultana: Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications (2022)

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