We propose an objective Bayesian algorithm for searching
the space of Gaussian directed acyclic graphical models when
the variables are assumed to satisfy a given ordering. The
approach used is based on non-local parameter priors and thus
it is suitable for learning sparse graphs. The algorithm is
implemented in C++ using the open-source library Armadillo.
| Version: |
1.0 |
| Depends: |
Rcpp (≥ 0.9.13), RcppArmadillo (≥ 0.3.2.4) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Published: |
2013-04-13 |
| Author: |
Davide Altomare, Guido Consonni and Luca La Rocca |
| Maintainer: |
Davide Altomare <davide.altomare at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| In views: |
gR |
| CRAN checks: |
FBFsearch results |