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 1045 articles , 1 standard article )

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  1. Aït Hennani, L.; Lemdani, Mohamed; Ould Saïd, E.: Robust regression analysis for a censored response and functional regressors (2019)
  2. Albarrán, Irene; Alonso-González, Pablo J.; Arribas-Gil, Ana; Grané, Aurea: How functional data can enhance the estimation of health expectancy: the case of disabled Spanish population (2019)
  3. Allam, Abdelaziz; Mourid, Tahar: Optimal rate for covariance operator estimators of functional autoregressive processes with random coefficients (2019)
  4. Alvarez, Agustín; Boente, Graciela; Kudraszow, Nadia: Robust sieve estimators for functional canonical correlation analysis (2019)
  5. Aneiros, Germán; Cao, Ricardo; Fraiman, Ricardo; Genest, Christian; Vieu, Philippe: Recent advances in functional data analysis and high-dimensional statistics (2019)
  6. Arnone, Eleonora; Azzimonti, Laura; Nobile, Fabio; Sangalli, Laura M.: Modeling spatially dependent functional data via regression with differential regularization (2019)
  7. Benhenni, Karim; Hassan, Ali Hajj; Su, Yingcai: Local polynomial estimation of regression operators from functional data with correlated errors (2019)
  8. Blanquero, R.; Carrizosa, E.; Jiménez-Cordero, A.; Martín-Barragán, B.: Functional-bandwidth kernel for support vector machine with functional data: an alternating optimization algorithm (2019)
  9. Bodnar, Taras; Okhrin, Ostap; Parolya, Nestor: Optimal shrinkage estimator for high-dimensional mean vector (2019)
  10. Boente, Graciela; Rodriguez, Daniela; Sued, Mariela: The spatial sign covariance operator: asymptotic results and applications (2019)
  11. Bongiorno, Enea G.; Goia, Aldo: Describing the concentration of income populations by functional principal component analysis on Lorenz curves (2019)
  12. Boudou, Alain; Viguier-Pla, Sylvie: Commuter of operators in a Hilbert space (2019)
  13. Cao, Jiguo; Soiaporn, Kunlaya; Carroll, Raymond J.; Ruppert, David: Modeling and prediction of multiple correlated functional outcomes (2019)
  14. Chaouch, Mohamed: Volatility estimation in a nonlinear heteroscedastic functional regression model with martingale difference errors (2019)
  15. Chen, Xuerong; Li, Haoqi; Liang, Hua; Lin, Huazhen: Functional response regression analysis (2019)
  16. Cuesta-Albertos, Juan A.; García-Portugués, Eduardo; Febrero-Bande, Manuel; González-Manteiga, Wenceslao: Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes (2019)
  17. Dai, Wenlin; Genton, Marc G.: Directional outlyingness for multivariate functional data (2019)
  18. del Barrio, Eustasio; Gordaliza, Paula; Lescornel, Hélène; Loubes, Jean-Michel: Central limit theorem and bootstrap procedure for Wasserstein’s variations with an application to structural relationships between distributions (2019)
  19. Descary, Marie-Hélène; Panaretos, Victor M.: Functional data analysis by matrix completion (2019)
  20. Febrero-Bande, Manuel; Galeano, Pedro; González-Manteiga, Wenceslao: Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random (2019)

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