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
CRAN checks: fdaPDE results


Reference manual: fdaPDE.pdf
Package source: fdaPDE_1.0-9.tar.gz
Windows binaries: r-devel: fdaPDE_1.0-9.zip, r-release: fdaPDE_1.0-9.zip, r-oldrel: fdaPDE_1.0-9.zip
macOS binaries: r-release: fdaPDE_1.0-9.tgz, r-oldrel: fdaPDE_1.0-9.tgz
Old sources: fdaPDE archive


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