Reparameterized and marginalized posterior sampling for conditional autoregressive models, OpenCL implementation

This package fits Bayesian conditional autoregressive models for spatial and spatiotemporal data on a lattice. It uses OpenCL kernels running on GPUs to perform rejection sampling to obtain independent samples from the joint posterior distribution of model parameters.


Reference manual

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0.1.4 by Kate Cowles, 7 years ago

Browse source code at

Authors: Kate Cowles and Michael Seedorff and Alex Sawyer

Documentation:   PDF Manual  

GPL (>= 3) license

Imports OpenCL, fields

Suggests coda

System requirements: OpenCL library; double-precision AMD or Nvidia GPU; GNU make

See at CRAN