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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("fdaPDE")

1.0-9 by Eleonora Arnone, 5 months ago


Browse source code at https://github.com/cran/fdaPDE


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