fpca

R package fpca: Restricted MLE for Functional Principal Components Analysis. A geometric approach to MLE for functional principal components

Keywords for this software

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References in zbMATH (referenced in 5 articles )

Showing results 1 to 5 of 5.
Sorted by year (citations)

  1. Tekwe, Carmen D.; Zoh, Roger S.; Bazer, Fuller W.; Wu, Guoyao; Carroll, Raymond J.: Functional multiple indicators, multiple causes measurement error models (2018)
  2. Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
  3. Rosales Marticorena, Francisco: Empirical Bayesian smoothing splines for signals with correlated errors: methods and applications (2016)
  4. Zhang, Wenfei; Wei, Ying: Regression based principal component analysis for sparse functional data with applications to screening growth paths (2015)
  5. Manuel Febrero-Bande; Manuel de la Fuente: Statistical Computing in Functional Data Analysis: The R Package fda.usc (2012) not zbMATH