Conducts hierarchical partitioning to calculate individual contributions of spatial and  predictors (groups) towards total R2  for spatial simultaneous autoregressive model.

A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.

Evaluates the statistical coherence of existing administrative
    partitions (e.g. inter-municipal groupings, districts) by identifying
    spatial units whose attributive profile is more similar to a neighbouring
    group than to their own. Border units are iteratively reassigned to the
    group they are most affine with, based on Euclidean or Mahalanobis
    distance computed on user-supplied numeric variables, with optional
    per-variable weighting and standardisation. Spatial contiguity is
    enforced throughout: isolated candidates are reintegrated into their
    original group, disconnected fragments are resolved, and empty groups
    are restored. Convergence is monitored via an eta-squared cohesion
    criterion. The resulting partition can be compared to the original
    administrative delineation using multilevel models, providing a
    quantitative measure of boundary inefficiency.

Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).