spMC: Continuous-Lag Spatial Markov Chains

Authors spMC logo

Luca Sartore

Maintainer: Luca Sartore

GPLv2 license DOI DOI CRAN RStudio mirror downloads Total Downloads from CRAN RStudio mirror

Features of the package

The main goal of the spMC package is to provide a set of functions 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.

Several functions are available for the stratum lengths analysis, in particular they compute the stratum lengths for each stratum category, they compute the empirical distributions and many other tools for a graphical analysis.

Usually, the basic inputs for the most of the functions are a vector of categorical data and their location coordinates. They are used to estimate empirical transition probabilities (transiogram), to estimate model parameters (tpfit for one-dimensional Markov chains or multi_tpfit for multidimensional Markov chains). Once parameters are estimated, it’s possible to compute theoretical transition probabilities by the use of the function predict.tpfit for one-dimensional Markov chains and predict.multi_tpfit for multidimensional ones.

The function plot.transiogram allows to plot one-dimensional transiograms, while image.multi_tpfit permit to draw transition probability maps. A powerful tool to explore graphically the anisotropy of such process is given by the functions pemt and image.pemt, which let the user to draw “quasi-empirical” transition probability maps.

Simulation methods are based on Indicator Kriging (sim_ik), Indicator Cokriging (sim_ck), Fixed or Random Path algorithms (sim_path) and Multinomial Categorical Simulation technique (sim_mcs).

For a complete list of exported functions, use library(help = "spMC") once the spMC package is installed.


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