fdaPDE: Functional Data Analysis and Partial Differential Equations (PDE); Statistical Analysis of Functional and Spatial Data, Based on Regression with PDE Regularization

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. See Sangalli, L. M. (2021). Spatial Regression With Partial Differential Equation Regularisation. International Statistical Review, 89(3), 505-531. for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

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Package details

AuthorEleonora Arnone [aut, cre], Laura M. Sangalli [aut], Eardi Lila [aut], Jim Ramsay [aut], Luca Formaggia [aut], Giovanni Ardenghi [ctb], Michele Cavazzutti [ctb], Aldo Clemente [ctb], Alessandra Colli [ctb], Alberto Colombo [ctb], Luca Colombo [ctb], Carlo de Falco [ctb], Enrico Dall'Acqua [ctb], Giulia Ferla [ctb], Lorenzo Ghilotti [ctb], Cristina Galimberti [ctb], Jiyoung Kim [ctb], Martina Massardi [ctb], Giorgio Meretti [ctb], Giulio Perin [ctb], Clara Pigolotti [ctb], Andrea Poiatti [ctb], Gian Matteo Rinaldi [ctb], Stefano Spaziani [ctb], Andrea Vicini [ctb]
MaintainerEleonora Arnone <eleonora.arnone@polimi.it>
LicenseCC BY-NC-SA 4.0
Package repositoryView on CRAN
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fdaPDE documentation built on Nov. 10, 2022, 5:06 p.m.