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 847 articles , 1 standard article )

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  1. Aflalo, Yonathan; Kimmel, Ron: Regularized principal component analysis (2017)
  2. Álvarez-Liébana, J.; Bosq, D.; Ruiz-Medina, M.D.: Asymptotic properties of a component-wise ARH(1) plug-in predictor (2017)
  3. Bárcenas, Roberto; Ortega, J.; Quiroz, A.J.: Quadratic forms of the empirical processes for the two-sample problem for functional data (2017)
  4. Benatia, David; Carrasco, Marine; Florens, Jean-Pierre: Functional linear regression with functional response (2017)
  5. Benhenni, Karim; Hedli-Griche, Sonia; Rachdi, Mustapha: Regression models with correlated errors based on functional random design (2017)
  6. Boente, Graciela; Vahnovan, Alejandra: Robust estimators in semi-functional partial linear regression models (2017)
  7. Cao, Jiguo; Wang, Liangliang; Huang, Zhongwen; Gai, Junyi; Wu, Rongling: Functional mapping of multiple dynamic traits (2017)
  8. Cardot, Hervé; Godichon-Baggioni, Antoine: Fast estimation of the median covariation matrix with application to online robust principal components analysis (2017)
  9. Cerovecki, Clément; Hörmann, Siegfried: On the CLT for discrete Fourier transforms of functional time series (2017)
  10. Chaouch, Mohamed; Laïb, Na^amane; Louani, Djamal: Rate of uniform consistency for a class of mode regression on functional stationary ergodic data (2017)
  11. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
  12. Cuesta-Albertos, Juan A.; Febrero-Bande, Manuel; Oviedo de la Fuente, Manuel: The $\mathrmDD^G$-classifier in the functional setting (2017)
  13. Dass, Sarat C.; Lee, Jaeyong; Lee, Kyoungjae; Park, Jonghun: Laplace based approximate posterior inference for differential equation models (2017)
  14. Delicado, Pedro; Vieu, Philippe: Choosing the most relevant level sets for depicting a sample of densities (2017)
  15. Demongeot, Jacques; Naceri, Amina; Laksaci, Ali; Rachdi, Mustapha: Local linear regression modelization when all variables are curves (2017)
  16. Ding, Hui; Liu, Yanghui; Xu, Wenchao; Zhang, Riquan: A class of functional partially linear single-index models (2017)
  17. Djehiche, Boualem; Nassar, Hiba: A functional Hodrick-Prescott filter (2017)
  18. Floriello, Davide; Vitelli, Valeria: Sparse clustering of functional data (2017)
  19. Ghiglietti, Andrea; Ieva, Francesca; Paganoni, Anna Maria; Aletti, Giacomo: On linear regression models in infinite dimensional spaces with scalar response (2017)
  20. Hann, Il-Horn; Oh, Joohee: Combating prerelease piracy: modeling the effects of antipiracy measures in P2P networks (2017)

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