<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Fabbri, P.</style></author><author><style face="normal" font="default" size="100%">Gaetan, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">spMC: an R-package for 3D Lithological Reconstructions Based on Spatial Markov Chains</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Geosciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0098300416301479</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">94</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil prediction/simulation in a plain site of the NE Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, in spMC package the most known estimation/simulation methods as indicator Kriging and CoKriging were implemented, but also most advanced methods such as path methods and Bayesian procedure exploiting the maximum entropy. Because the spMC package was thought for intensive geostatistical computations, part of the code is implemented with parallel computing via the OpenMP constructs, allowing to deal with more than five lithologies, but trying to keep a computational efficiency. A final analysis of this computational efficiency&amp;nbsp;of spMC compares the prediction/simulation results using different numbers of CPU cores, considering the example data set of the case study available in the package.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">40-47</style></section></record></records></xml>