Spatial Implementation of Bayesian Networks and Mapping

Allows spatial implementation of Bayesian networks and mapping in geographical space. It makes maps of expected value (or most likely state) given known and unknown conditions, maps of uncertainty measured as coefficient of variation or Shannon index (entropy), maps of probability associated to any states of any node of the network. Some additional features are provided as well: parallel processing options, data discretization routines and function wrappers designed for users with minimal knowledge of the R language. Outputs can be exported to any common GIS format. Development was funded by the European Union FP7 (2007-2013), under project ROBIN (<>).



  • New mantainer's email address in the DESCRIPTION file
  • Minor fix to html documentation format
  • Slight speed improvements with better use of internal functions


  • Function mapTarget (and wrapper bnspatial) improved to write large spatial data without oveloading memory
  • Improved robustness in handling input arguments and error/warning messages for most functions
  • Swapped an internal function to compile the network inside loadNetwork
  • Changed objects name within example dataset to show tidily in documentation and minor changes to examples
  • Added file
  • Added copyright notes in COPYRIGHT file
  • Fixed author list to CRAN standards
  • Improved and extended documentation (and code comments)
  • Added startup message with copyright notice and acknowledgments


  • First release on CRAN

Reference manual

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1.0.2 by Dario Masante, 3 months ago

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Browse source code at

Authors: Dario Masante [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports raster, gRbase, gRain, doParallel, foreach

Suggests knitr, rmarkdown

See at CRAN