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 900 articles , 1 standard article )

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  1. Agostinelli, Claudio: Local half-region depth for functional data (2018)
  2. Banerjee, Buddhananda; Mazumder, Satyaki: A more powerful test identifying the change in mean of functional data (2018)
  3. Barrow, Devon; Kourentzes, Nikolaos: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days (2018)
  4. Cao, Guanqun; Wang, Li: Simultaneous inference for the mean of repeated functional data (2018)
  5. Choi, Ji Yeh; Hwang, Heungsun; Timmerman, Marieke E.: Functional parallel factor analysis for functions of one- and two-dimensional arguments (2018)
  6. Combettes, Patrick L.; Salzo, Saverio; Villa, Silvia: Regularized learning schemes in feature Banach spaces (2018)
  7. Ding, Hui; Zhang, Riquan; Zhang, Jian: Quantile estimation for a hybrid model of functional and varying coefficient regressions (2018)
  8. Giraldo, Ramón; Dabo-Niang, Sophie; Martínez, Sergio: Statistical modeling of spatial big data: an approach from a functional data analysis perspective (2018)
  9. Górecki, Tomasz; Krzyśko, Mirosław; Waszak, Łukasz; Wołyński, Waldemar: Selected statistical methods of data analysis for multivariate functional data (2018)
  10. Hargreaves, Jessica K.; Knight, Marina I.; Pitchford, Jon W.; Oakenfull, Rachael J.; Davis, Seth J.: Clustering nonstationary Circadian rhythms using locally stationary wavelet representations (2018)
  11. Henien, Aicha; Ait-Hennani, Larbi; Demongeot, Jacques; Laksaci, Ali; Rachdi, Mustapha: Heteroscedasticity test when the covariables are functionals (2018)
  12. Imaizumi, Masaaki; Kato, Kengo: PCA-based estimation for functional linear regression with functional responses (2018)
  13. Lin, Zhenhua; Müller, Hans-Georg; Yao, Fang: Mixture inner product spaces and their application to functional data analysis (2018)
  14. Liu, Baisen; Wang, Liangliang; Cao, Jiguo: Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes (2018)
  15. Manrique, Tito; Crambes, Christophe; Hilgert, Nadine: Ridge regression for the functional concurrent model (2018)
  16. Nie, Yunlong; Wang, Liangliang; Liu, Baisen; Cao, Jiguo: Supervised functional principal component analysis (2018)
  17. Qi, Xin; Luo, Ruiyan: Function-on-function regression with thousands of predictive curves (2018)
  18. Quarteroni, Alfio: The role of statistics in the era of big data: a computational scientist’ perspective (2018)
  19. Roche, Angelina: Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety (2018)
  20. Secchi, Piercesare: On the role of statistics in the era of big data: a call for a debate (2018)

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