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 1009 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. Allam, Abdelaziz; Mourid, Tahar: Optimal rate for covariance operator estimators of functional autoregressive processes with random coefficients (2019)
  3. 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)
  4. Chen, Xuerong; Li, Haoqi; Liang, Hua; Lin, Huazhen: Functional response regression analysis (2019)
  5. Dai, Wenlin; Genton, Marc G.: Directional outlyingness for multivariate functional data (2019)
  6. 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)
  7. 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)
  8. Fu, Eric; Heckman, Nancy: Model-based curve registration via stochastic approximation EM algorithm (2019)
  9. Grollemund, Paul-Marie; Abraham, Christophe; Baragatti, Meïli; Pudlo, Pierre: Bayesian functional linear regression with sparse step functions (2019)
  10. Liebl, Dominik; Rameseder, Stefan: Partially observed functional data: the case of systematically missing parts (2019)
  11. Lin, Hongmei; Jiang, Xuejun; Lian, Heng; Zhang, Weiping: Reduced rank modeling for functional regression with functional responses (2019)
  12. Martínez-Hernández, Israel; Genton, Marc G.; González-Farías, Graciela: Robust depth-based estimation of the functional autoregressive model (2019)
  13. Sang, Peijun; Wang, Liangliang; Cao, Jiguo: Weighted empirical likelihood inference for dynamical correlations (2019)
  14. Song, Joon Jin; Mallick, Bani: Hierarchical Bayesian models for predicting spatially correlated curves (2019)
  15. Wang, Bo; Xu, Aiping: Gaussian process methods for nonparametric functional regression with mixed predictors (2019)
  16. Wong, Raymond K. W.; Zhang, Xiaoke: Nonparametric operator-regularized covariance function estimation for functional data (2019)
  17. Abramowicz, Konrad; Häger, Charlotte K.; Pini, Alessia; Schelin, Lina; de Luna, Sara Sjöstedt; Vantini, Simone: Nonparametric inference for functional-on-scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament (2018)
  18. Agostinelli, Claudio: Local half-region depth for functional data (2018)
  19. Backenroth, Daniel; Goldsmith, Jeff; Harran, Michelle D.; Cortes, Juan C.; Krakauer, John W.; Kitago, Tomoko: Modeling motor learning using heteroscedastic functional principal components analysis (2018)
  20. Banerjee, Buddhananda; Mazumder, Satyaki: A more powerful test identifying the change in mean of functional data (2018)

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