fdaPDE: Physics-Informed Spatial and Functional Data Analysis

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) <doi:10.1111/insr.12444> "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

Getting started

Package details

AuthorEleonora Arnone [aut, cre], Aldo Clemente [aut], Laura M. Sangalli [aut], Eardi Lila [aut], Jim Ramsay [aut], Luca Formaggia [aut], Giovanni Ardenghi [ctb], Blerta Begu [ctb], Michele Cavazzutti [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], Simone Panzeri [ctb], Giulio Perin [ctb], Clara Pigolotti [ctb], Andrea Poiatti [ctb], Gian Matteo Rinaldi [ctb], Stefano Spaziani [ctb], Andrea Vicini [ctb], David C. Sterratt [cph] (Author of included Triangle source files)
MaintainerEleonora Arnone <eleonora.arnone@polimi.it>
LicenseGPL-3
Version1.1-16
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("fdaPDE")

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fdaPDE documentation built on March 7, 2023, 5:28 p.m.