fdaPDE: 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.
| Version: |
1.0-9 |
| Depends: |
R (≥ 3.5.0), stats, grDevices, graphics, geometry, rgl, Matrix, plot3D, plot3Drgl |
| LinkingTo: |
RcppEigen |
| Suggests: |
MASS, testthat |
| Published: |
2020-05-15 |
| Author: |
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] |
| Maintainer: |
Eleonora Arnone <eleonora.arnone at polimi.it> |
| License: |
CC BY-NC-SA 4.0 |
| Copyright: |
See the individual source files for copyrights information |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11 |
| Materials: |
README NEWS |
| In views: |
FunctionalData |
| CRAN checks: |
fdaPDE results |
Documentation:
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