Methods and data for cluster detection and disease mapping.

Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.

Finite element modeling (FEM) uses meshes of triangles to define surfaces.  
  A surface within a triangle may be either linear or quadratic. 
  In the order one case each node in the mesh is associated with a basis 
  function and the basis is called the order one finite element basis.
  In the order two case each edge mid-point is also associated with a basis
  function.  Functions are provided for smoothing, density function estimation
  point evaluation and plotting results.  Two papers illustrating the 
  finite element data analysis are Sangalli, L.M., Ramsay, J.O., 
  Ramsay, T.O. (2013)<http://www.mox.polimi.it/~sangalli> and
  Bernardi, M.S, Carey, M., Ramsay, J. O., Sangalli, L. 
  (2018)<http://www.mox.polimi.it/~sangalli>. Modelling 
  spatial anisotropy via regression with partial differential 
  regularization Journal of Multivariate Analysis, 167, 15-30.

Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE'
	R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, 
	such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial 
	interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and 
	differential expression analysis using spatial mixed models.