CARrampsOcl: 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.

Version: 0.1.4
Imports: OpenCL, fields
Suggests: coda
Published: 2013-10-25
Author: Kate Cowles and Michael Seedorff and Alex Sawyer
Maintainer: Kate Cowles <kate-cowles at uiowa.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: OpenCL library; double-precision AMD or Nvidia GPU; GNU make
Materials: INSTALL
CRAN checks: CARrampsOcl results

Downloads:

Reference manual: CARrampsOcl.pdf
Package source: CARrampsOcl_0.1.4.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
OS X Snow Leopard binaries: r-release: not available, r-oldrel: CARrampsOcl_0.1.4.tgz
OS X Mavericks binaries: r-release: not available
Old sources: CARrampsOcl archive