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 1149 articles , 1 standard article )

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  1. Helander, Sami; Van Bever, Germain; Rantala, Sakke; Ilmonen, Pauliina: Pareto depth for functional data (2020)
  2. Huang, Shih-Feng; Guo, Meihui; Chen, May-Ru: Stock market trend prediction using a functional time series approach (2020)
  3. Hu, Yuping; Xue, Liugen; Zhao, Jing; Zhang, Liying: Skew-normal partial functional linear model and homogeneity test (2020)
  4. Virta, Joni; Li, Bing; Nordhausen, Klaus; Oja, Hannu: Independent component analysis for multivariate functional data (2020)
  5. Wang, Jiangyan; Cao, Guanqun; Wang, Li; Yang, Lijian: Simultaneous confidence band for stationary covariance function of dense functional data (2020)
  6. Zhang, Fode; Zhang, Weiping; Li, Rui; Lian, Heng: Faster convergence rate for functional linear regression in reproducing kernel Hilbert spaces (2020)
  7. Zhang, Likun; del Castillo, Enrique; Berglund, Andrew J.; Tingley, Martin P.; Govind, Nirmal: Computing confidence intervals from massive data via penalized quantile smoothing splines (2020)
  8. Zhang, Tingting; Sun, Yinge; Li, Huazhang; Yan, Guofen; Tanabe, Seiji; Miao, Ruizhong; Wang, Yaotian; Caffo, Brian S.; Quigg, Mark S.: Bayesian inference of a directional brain network model for intracranial EEG data (2020)
  9. Zhou, Jianjun; Peng, Qingyan: Estimation for functional partial linear models with missing responses (2020)
  10. Aït Hennani, L.; Lemdani, Mohamed; Ould Saïd, E.: Robust regression analysis for a censored response and functional regressors (2019)
  11. Al-Awadhi, Fahimah A.; Kaid, Zoulikha; Laksaci, Ali; Ouassou, Idir; Rachdi, Mustapha: Functional data analysis: local linear estimation of the (L_1)-conditional quantiles (2019)
  12. 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)
  13. Allam, Abdelaziz; Mourid, Tahar: Optimal rate for covariance operator estimators of functional autoregressive processes with random coefficients (2019)
  14. Alvarez, Agustín; Boente, Graciela; Kudraszow, Nadia: Robust sieve estimators for functional canonical correlation analysis (2019)
  15. Aneiros, Germán; Cao, Ricardo; Fraiman, Ricardo; Genest, Christian; Vieu, Philippe: Recent advances in functional data analysis and high-dimensional statistics (2019)
  16. Arnone, Eleonora; Azzimonti, Laura; Nobile, Fabio; Sangalli, Laura M.: Modeling spatially dependent functional data via regression with differential regularization (2019)
  17. Barboza, Luis A.; Emile-Geay, Julien; Li, Bo; He, Wan: Efficient reconstructions of Common Era climate via integrated nested Laplace approximations (2019)
  18. Benhenni, Karim; Hassan, Ali Hajj; Su, Yingcai: Local polynomial estimation of regression operators from functional data with correlated errors (2019)
  19. 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)
  20. Bodnar, Taras; Okhrin, Ostap; Parolya, Nestor: Optimal shrinkage estimator for high-dimensional mean vector (2019)

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