The R Journal: article published in 2017, volume 9:2

anchoredDistr: a Package for the Bayesian Inversion of Geostatistical Parameters with Multi-type and Multi-scale Data
Heather Savoy, Falk Heße and Yoram Rubin , The R Journal (2017) 9:2, pages 6-17.

Abstract The Method of Anchored Distributions (MAD) is a method for Bayesian inversion designed for inferring both local (e.g. point values) and global properties (e.g. mean and variogram parameters) of spatially heterogenous fields using multi-type and multi-scale data. Software implementations of MAD exist in C++ and C# to import data, execute an ensemble of forward model simulations, and perform basic post-processing of calculating likelihood and posterior distributions for a given application. This article describes the R package anchoredDistr that has been built to provide an R based environment for this method. In particular, anchoredDistr provides a range of post-processing capabilities for MAD software by taking advantage of the statistical capabilities and wide use of the R language. Two examples from stochastic hydrogeology are provided to highlight the features of the package for MAD applications in inferring anchored distributions of local parameters (e.g. point values of transmissivity) as well as global parameters (e.g. the mean of the spatial random function for hydraulic conductivity).

Received: 2016-08-25; online 2017-06-28, supplementary material, (489 B)
CRAN packages: gstat, spBayes, spTimer, anchoredDistr, devtools, RSQLite, np, plyr, dplyr, ggplot2
CRAN Task Views implied by cited CRAN packages: Spatial, SpatioTemporal, Bayesian, Econometrics, Graphics, Phylogenetics, SocialSciences, TimeSeries

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-034,
  author = {Heather Savoy and Falk Heße and Yoram Rubin},
  title = {{anchoredDistr: a Package for the Bayesian Inversion of
          Geostatistical Parameters with Multi-type and Multi-scale
          Data}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-034},
  url = {https://doi.org/10.32614/RJ-2017-034},
  pages = {6--17},
  volume = {9},
  number = {2}
}