R package GPFDA: Gaussian Process for Functional Data Analysis. Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on <https://github.com/gpfda/GPFDA-dev>.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Leroy, Arthur; Latouche, Pierre; Guedj, Benjamin; Gey, Servane: MAGMA: inference and prediction using multi-task Gaussian processes with common mean (2022)
- Evandro Konzen, Yafeng Cheng, Jian Qing Shi: Gaussian Process for Functional Data Analysis: The GPFDA Package for R (2021) arXiv
- Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
- Sung, Chih-Li; Hung, Ying; Rittase, William; Zhu, Cheng; Jeff Wu, C. F.: A generalized Gaussian process model for computer experiments with binary time series (2020)
- Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
- Greven, Sonja; Scheipl, Fabian: A general framework for functional regression modelling (2017)
- Jingjing Yang, Peng Ren: BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis (2016) arXiv
- Shi, Jian Qing; Choi, Taeryon: Gaussian process regression analysis for functional data (2011)