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 1215 articles , 1 standard article )

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  1. Basellini, Ugofilippo; Kjærgaard, Søren; Camarda, Carlo Giovanni: An age-at-death distribution approach to forecast cohort mortality (2020)
  2. Bongiorno, E. G.; Goia, A.; Vieu, P.: Estimating the complexity index of functional data: some asymptotics (2020)
  3. Bouzebda, Salim; Nemouchi, Boutheina: Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional (U)-statistics involving functional data (2020)
  4. Cheng, Yafeng; Shi, Jian Qing; Eyre, Janet: Nonlinear mixed-effects scalar-on-function models and variable selection (2020)
  5. Chen, Yaqing; Dawson, Matthew; Müller, Hans-Georg: Rank dynamics for functional data (2020)
  6. Dai, Wenlin; Mrkvička, Tomáš; Sun, Ying; Genton, Marc G.: Functional outlier detection and taxonomy by sequential transformations (2020)
  7. Darabi, Nadiyeh; Hosseini-Nasab, S. Mohammad E.: Projection-based classification for functional data (2020)
  8. Derrar, Saliha; Laksaci, Ali; Saïd, Elias Ould: (M)-estimation of the regression function under random left truncation and functional time series model (2020)
  9. Fang, Kuangnan; Zhang, Xiaochen; Ma, Shuangge; Zhang, Qingzhao: Smooth and locally sparse estimation for multiple-output functional linear regression (2020)
  10. Helander, Sami; Van Bever, Germain; Rantala, Sakke; Ilmonen, Pauliina: Pareto depth for functional data (2020)
  11. Huang, Shih-Feng; Guo, Meihui; Chen, May-Ru: Stock market trend prediction using a functional time series approach (2020)
  12. Hu, Yuping; Xue, Liugen; Zhao, Jing; Zhang, Liying: Skew-normal partial functional linear model and homogeneity test (2020)
  13. Ieva, Francesca; Paganoni, Anna Maria: Component-wise outlier detection methods for robustifying multivariate functional samples (2020)
  14. Jones, Ben; Artemiou, Andreas: On principal components regression with Hilbertian predictors (2020)
  15. Kim, Joonpyo; Oh, Hee-Seok: Pseudo-quantile functional data clustering (2020)
  16. Kobayashi, Kei; Wynn, Henry P.: Empirical geodesic graphs and CAT((k)) metrics for data analysis (2020)
  17. Koltchinskii, Vladimir; Löffler, Matthias; Nickl, Richard: Efficient estimation of linear functionals of principal components (2020)
  18. Ling, Nengxiang; Aneiros, Germán; Vieu, Philippe: (k)NN estimation in functional partial linear modeling (2020)
  19. Ling, Nengxiang; Wang, Lingyu; Vieu, Philippe: Convergence rate of kernel regression estimation for time series data when both response and covariate are functional (2020)
  20. Li, Zheyuan; Wood, Simon N.: Faster model matrix crossproducts for large generalized linear models with discretized covariates (2020)

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