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., Ramsay, J.O., Ramsay, T.O. (2013), Spatial spline regression models for an overview.


Reference manual

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1.0-9 by Eleonora Arnone, a year ago

Browse source code at

Authors: Eardi Lila [aut] , Laura M. Sangalli [aut] , Eleonora Arnone [aut, cre] , Jim Ramsay [aut] , Luca Formaggia [aut] , Alessandra Colli [ctb] , Luca Colombo [ctb] , Carlo de Falco [ctb]

Documentation:   PDF Manual  

Task views:

CC BY-NC-SA 4.0 license

Depends on stats, grDevices, graphics, geometry, rgl, Matrix, plot3D, plot3Drgl

Suggests MASS, testthat

Linking to RcppEigen

System requirements: C++11

See at CRAN