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 1332 articles , 1 standard article )

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  1. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
  2. Kolkiewicz, Adam; Rice, Gregory; Xie, Yijun: Projection pursuit based tests of normality with functional data (2021)
  3. Krebs, Johannes: A note on exponential inequalities in Hilbert spaces for spatial processes with applications to the functional kernel regression model (2021)
  4. Lai, Tingyu; Zhang, Zhongzhan; Wang, Yafei; Kong, Linglong: Testing independence of functional variables by angle covariance (2021)
  5. Li, Rui; Lu, Wenqi; Zhu, Zhongyi; Lian, Heng: Optimal prediction of quantile functional linear regression in reproducing kernel Hilbert spaces (2021)
  6. Nagy, Stanislav; Helander, Sami; van Bever, Germain; Viitasaari, Lauri; Ilmonen, Pauliina: Flexible integrated functional depths (2021)
  7. Steven Golovkine: FDApy: a Python package for functional data (2021) arXiv
  8. Aguilera-Morillo, M. Carmen; Aguilera, Ana M.: Multi-class classification of biomechanical data: a functional LDA approach based on multi-class penalized functional PLS (2020)
  9. Arnone, Eleonora; Kneip, Alois; Nobile, Fabio; Sangalli, Laura M.: Some numerical test on the convergence rates of regression with differential regularization (2020)
  10. Aue, Alexander; van Delft, Anne: Testing for stationarity of functional time series in the frequency domain (2020)
  11. Barahona, S.; Centella, P.; Gual-Arnau, X.; Ibáñez, M. V.; Simó, A.: Supervised classification of geometrical objects by integrating currents and functional data analysis (2020)
  12. Barahona, Sonia; Centella, Pablo; Gual-Arnau, Ximo; Ibáñez, M. Victoria; Simó, Amelia: Generalized linear models for geometrical current predictors: an application to predict garment fit (2020)
  13. Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
  14. Basellini, Ugofilippo; Kjærgaard, Søren; Camarda, Carlo Giovanni: An age-at-death distribution approach to forecast cohort mortality (2020)
  15. Bharath, Karthik; Kurtek, Sebastian: Distribution on warp maps for alignment of open and closed curves (2020)
  16. Bongiorno, E. G.; Goia, A.; Vieu, P.: Estimating the complexity index of functional data: some asymptotics (2020)
  17. Bouzebda, Salim; Nemouchi, Boutheina: Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional (U)-statistics involving functional data (2020)
  18. Bretó, Carles; Ionides, Edward L.; King, Aaron A.: Panel data analysis via mechanistic models (2020)
  19. Cabero, Ismael; Epifanio, Irene: Finding archetypal patterns for binary questionnaires (2020)
  20. Cheng, Yafeng; Shi, Jian Qing; Eyre, Janet: Nonlinear mixed-effects scalar-on-function models and variable selection (2020)

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