fdarep for Functional Data Analysis
Poorbita Kundu pkundu@ucdavis.edu Changbo Zhu Kehui Chen Pedro Delicado Su I Iao Hang Zhou Han Chen Muqing Cui
Hans-Georg Müller hgmueller@ucdavis.edu Jane-Ling Wang janelwang@ucdavis.edu
fdarep is a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) for repeated functional data.
References: Chen, K., Delicado, P., & Müller, H. G. (2017). Modelling function-valued stochastic processes, with applications to fertility dynamics. Journal of the Royal Statistical Society Series B: Statistical Methodology, 79(1), 177-196. Chen, K., & Müller, H. G. (2012). Modeling repeated functional observations. Journal of the American Statistical Association, 107(500), 1599-1609. Hall, P., Müller, H. G., & Wang, J. L. (2006). Properties of principal component methods for functional and longitudinal data analysis. Yao, F., Müller, H. G., & Wang, J. L. (2005). Functional data analysis for sparse longitudinal data. Journal of the American statistical association, 100(470), 577-590.
fdarep is a comprehensive package that directly implements fitting of the following models for repeated functional data: -- Two-dimensional FPCA for dense repeated functional data -- Marginal FPCA for dense repeated functional data -- Product FPCA for dense repeated functional data -- Marginal FPCA for sparse repeated functional data -- Product FPCA for sparse repeated functional data
Maintainer: Poorbita Kundu pkundu@ucdavis.edu
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