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.


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install.packages("CARrampsOcl")

0.1.4 by Kate Cowles, 6 years ago


Browse source code at https://github.com/cran/CARrampsOcl


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