R package fdaPDE: Functional Data Analysis and Partial Differential Equations; Statistical Analysis of Functional and Spatial Data, Based on Regression with Partial Differential Regularizations. An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Ferraccioli, Federico; Sangalli, Laura M.; Finos, Livio: Some first inferential tools for spatial regression with differential regularization (2022)
- Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
- Arnone, Eleonora; Azzimonti, Laura; Nobile, Fabio; Sangalli, Laura M.: Modeling spatially dependent functional data via regression with differential regularization (2019)
- Bernardi, Mara S.; Carey, Michelle; Ramsay, James O.; Sangalli, Laura M.: Modeling spatial anisotropy via regression with partial differential regularization (2018)
- Clara Happ: Object-Oriented Software for Functional Data (2017) arXiv
- Menafoglio, Alessandra; Secchi, Piercesare: Statistical analysis of complex and spatially dependent data: a review of object oriented spatial statistics (2017)