Utilities to support spatial data manipulation, query, sampling
    and modelling in ecological applications. Functions include models for species 
	population density, spatial smoothing, multivariate separability, point process 
	model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and 
	analysis, auto-logistic modeling, sampling models, cluster optimization, statistical 
	exploratory tools and raster-based metrics.

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.