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 777 articles , 1 standard article )

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  1. Aflalo, Yonathan; Kimmel, Ron: Regularized principal component analysis (2017)
  2. Chaouch, Mohamed; Laïb, Na^amane; Louani, Djamal: Rate of uniform consistency for a class of mode regression on functional stationary ergodic data (2017)
  3. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
  4. Dass, Sarat C.; Lee, Jaeyong; Lee, Kyoungjae; Park, Jonghun: Laplace based approximate posterior inference for differential equation models (2017)
  5. Demongeot, Jacques; Naceri, Amina; Laksaci, Ali; Rachdi, Mustapha: Local linear regression modelization when all variables are curves (2017)
  6. Ghiglietti, Andrea; Ieva, Francesca; Paganoni, Anna Maria; Aletti, Giacomo: On linear regression models in infinite dimensional spaces with scalar response (2017)
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  8. Nanty, Simon; Helbert, Céline; Marrel, Amandine; Pérot, Nadia; Prieur, Clémentine: Uncertainty quantification for functional dependent random variables (2017)
  9. Romano, Elvira; Balzanella, Antonio; Verde, Rosanna: Spatial variability clustering for spatially dependent functional data (2017)
  10. Wang, Guochang: Dimension reduction in functional regression with categorical predictor (2017)
  11. Wang, Guochang; Zhou, Jianjun; Wu, Wuqing; Chen, Min: Robust functional sliced inverse regression (2017)
  12. Aletti, Giacomo; May, Caterina; Tommasi, Chiara: Best estimation of functional linear models (2016)
  13. Álvarez-Liébana, Javier; Bosq, Denis; Ruiz-Medina, María D.: Consistency of the plug-in functional predictor of the Ornstein-Uhlenbeck process in Hilbert and Banach spaces (2016)
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  15. Attaoui, Said; Ling, Nengxiang: Asymptotic results of a nonparametric conditional cumulative distribution estimator in the single functional index modeling for time series data with applications (2016)
  16. Benhenni, Karim; Su, Yingcai: Optimal sampling designs for nonparametric estimation of spatial averages of random fields (2016)
  17. Benziadi, Fatima; Laksaci, Ali; Tebboune, Fethallah: Note on conditional quantiles for functional ergodic data (2016)
  18. Beran, Jan; Liu, Haiyan: Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors (2016)
  19. Beran, Jan; Liu, Haiyan; Telkmann, Klaus: On two sample inference for eigenspaces in functional data analysis with dependent errors (2016)
  20. Berkes, István; Horváth, Lajos; Rice, Gregory: On the asymptotic normality of kernel estimators of the long run covariance of functional time series (2016)

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