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 867 articles , 1 standard article )

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  1. Agostinelli, Claudio: Local half-region depth for functional data (2018)
  2. Imaizumi, Masaaki; Kato, Kengo: PCA-based estimation for functional linear regression with functional responses (2018)
  3. Nie, Yunlong; Wang, Liangliang; Liu, Baisen; Cao, Jiguo: Supervised functional principal component analysis (2018)
  4. Qi, Xin; Luo, Ruiyan: Function-on-function regression with thousands of predictive curves (2018)
  5. Roche, Angelina: Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety (2018)
  6. Shang, Han Lin: Bootstrap methods for stationary functional time series (2018)
  7. Zhang, Xin; Wang, Chong; Wu, Yichao: Functional envelope for model-free sufficient dimension reduction (2018)
  8. Aflalo, Yonathan; Kimmel, Ron: Regularized principal component analysis (2017)
  9. Álvarez-Liébana, J.; Bosq, D.; Ruiz-Medina, M.D.: Asymptotic properties of a component-wise ARH(1) plug-in predictor (2017)
  10. Bárcenas, Roberto; Ortega, J.; Quiroz, A.J.: Quadratic forms of the empirical processes for the two-sample problem for functional data (2017)
  11. Benatia, David; Carrasco, Marine; Florens, Jean-Pierre: Functional linear regression with functional response (2017)
  12. Benhenni, Karim; Hedli-Griche, Sonia; Rachdi, Mustapha: Regression models with correlated errors based on functional random design (2017)
  13. Boente, Graciela; Vahnovan, Alejandra: Robust estimators in semi-functional partial linear regression models (2017)
  14. Brockhaus, Sarah; Melcher, Michael; Leisch, Friedrich; Greven, Sonja: Boosting flexible functional regression models with a high number of functional historical effects (2017)
  15. Cao, Jiguo; Wang, Liangliang; Huang, Zhongwen; Gai, Junyi; Wu, Rongling: Functional mapping of multiple dynamic traits (2017)
  16. Cardot, Hervé; Godichon-Baggioni, Antoine: Fast estimation of the median covariation matrix with application to online robust principal components analysis (2017)
  17. Cerovecki, Clément; Hörmann, Siegfried: On the CLT for discrete Fourier transforms of functional time series (2017)
  18. Chaouch, Mohamed; Laïb, Na^amane; Louani, Djamal: Rate of uniform consistency for a class of mode regression on functional stationary ergodic data (2017)
  19. Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S.: A unified approach to functional principal component analysis and functional multiple-set canonical correlation (2017)
  20. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv

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