R package fdapace. Provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the ’Eigen’ C++ library for numerical linear algebra and ’RcppEigen’ ”glue”.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Roy, Arkaprava; Ghosal, Subhashis: Optimal Bayesian smoothing of functional observations over a large graph (2022)
- Boente, Graciela; Salibián-Barrera, Matías: Robust functional principal components for sparse longitudinal data (2021)
- Cai, Xiong; Xue, Liugen; Pu, Xiaolong; Yan, Xingyu: Efficient estimation for varying-coefficient mixed effects models with functional response data (2021)
- Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
- Rha, Hyungmin; Kao, Ming-Hung; Pan, Rong: Bagging-enhanced sampling schedule for functional quadratic regression (2021)
- Rha, Hyungmin; Kao, Ming-Hung; Pan, Rong: Design optimal sampling plans for functional regression models (2020)
- Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv