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 1403 articles , 1 standard article )

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  1. Aguilera, Ana M.; Acal, Christian; Aguilera-Morillo, M. Carmen; Jiménez-Molinos, Francisco; Roldán, Juan B.: Homogeneity problem for basis expansion of functional data with applications to resistive memories (2021)
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  6. Carey, M.; Ramsay, J. O.: Fast stable parameter estimation for linear dynamical systems (2021)
  7. Chakraborty, Anirvan; Panaretos, Victor M.: Functional registration and local variations: identifiability, rank, and tuning (2021)
  8. Comte, Fabienne; Genon-Catalot, Valentine: Nonparametric estimation for i.i.d. Gaussian continuous time moving average models (2021)
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  10. Dette, Holger; Kutta, Tim: Detecting structural breaks in eigensystems of functional time series (2021)
  11. Estévez-Pérez, Graciela; Vieu, Philippe: A new way for ranking functional data with applications in diagnostic test (2021)
  12. Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
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  15. Hellmayr, Christoph; Gelfand, Alan E.: A partition Dirichlet process model for functional data analysis (2021)
  16. Hoffhues, M.; Römisch, W.; Surowiec, T. M.: On quantitative stability in infinite-dimensional optimization under uncertainty (2021)
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  18. Jeon, Jeong Min; Park, Byeong U.; Van Keilegom, Ingrid: Additive regression for predictors of various natures and possibly incomplete Hilbertian responses (2021)
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