An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

Build custom Europe SpatialPolygonsDataFrame, if you don't know what is
    a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. 
    Antares is a powerful software developed by RTE to simulate and study electric power systems 
    (more information about 'Antares' here: <https://antares-simulator.org/>).

A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Bayesian Markov chain Monte Carlo (MCMC) estimation of spatial
    panel data models including Spatial Autoregressive (SAR), Spatial Durbin
    Model (SDM), Spatial Error Model (SEM), Spatial Durbin Error Model (SDEM),
    and Spatial Lag of X (SLX) specifications with fixed effects. Supports
    convex combinations of multiple spatial weight matrices and Bayesian Model
    Averaging (BMA) over subsets of weight matrices. Implements the convex
    combination spatial weight matrix methodology of Debarsy and LeSage (2021)
    <doi:10.1080/07350015.2020.1840993> and the Bayesian spatial panel data
    models of LeSage and Pace (2009, ISBN:9781420064247).