Physics-Informed Spatial and Functional Data Analysis
Covariate test function for the horseshoe domain
Create a FEM basis
Create a 1.5D linear network mesh
Create a mesh.2.5D
object from the nodes locations and the connectiv...
Create a 2D triangular mesh
Create a mesh.3D
object from the connectivity matrix and nodes locat...
Nonparametric density estimation with differential regularization
Nonparametric spatio-temporal density estimation with differential reg...
Density initialization
Spatio-temporal density initialization
Evaluate a FEM object at a set of point locations
Evaluate a FEM.time object at a set of point locations
Deprecated Functions
Define a surface or spatial field by a Finite Element basis expansion
Define a spatio-temporal field by a Finite Element basis expansion
Smooth Functional Principal Component Analysis
FELSPLINE 3D test function
FELSPLINE test function
Image Plot of a 2D FEM object
Image plot of a 2D FEM.time object at a given time
Class for inference data
Constructor for inferenceDataObject class
Class for inference data in ST case
Constructor for inferenceDataObjectTime class
Plot a FEM
object
Plot a FEM.time
object at a given time
Plot a mesh.1.5D object
Plot a mesh.2.5D object
Plot a mesh.2D object
Plot a mesh.3D object
Project 2D points onto 1.5D linear network mesh
Project 3D points onto 2D 2.5D triangular mesh
Create a mesh.1.5D
object by splitting each edge of a given mesh int...
Create a mesh.2.5D
object by splitting each triangle of a given mesh...
Create a mesh.2D
object by splitting each triangle of a given mesh i...
Create a mesh.3D
object by splitting each tetrahedron of a given mes...
Refine 1.5D mesh
Refine a 2D triangular mesh
Spatial regression with differential regularization
Space-time regression with differential 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. (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.