Implement some models for
correlation/covariance matrices including two approaches
to model correlation matrices from a graphical structure.
One use latent parent variables as proposed in
Sterrantino et. al. (2024) <doi:10.48550/arXiv.2312.06289>.
The other uses a graph to specify conditional
relations between the variables.
The graphical structure makes correlation matrices
interpretable and avoids the quadratic increase of
parameters as a function of the dimension.
In the first approach a natural sequence of simpler
models along with a complexity penalization is used.
The second penalizes deviations from a base model.
These can be used as prior for model parameters,
considering C code through the 'cgeneric' interface
for the 'INLA' package (<https://www.r-inla.org>).
This allows one to use these models as building
blocks combined and to other latent Gaussian models
in order to build complex data models.
| Version: |
0.1.12 |
| Depends: |
R (≥ 4.3), Matrix, graph, numDeriv |
| Imports: |
methods, stats, utils, Rgraphviz |
| Suggests: |
INLA (≥ 24.02.09) |
| Published: |
2025-04-27 |
| DOI: |
10.32614/CRAN.package.graphpcor |
| Author: |
Elias Krainski 
[cre, aut, cph],
Denis Rustand
[aut, cph],
Anna Freni-Sterrantino
[aut, cph],
Janet van Niekerk
[aut, cph],
Haavard Rue’
[aut] |
| Maintainer: |
Elias Krainski <eliaskrainski at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Additional_repositories: |
https://inla.r-inla-download.org/R/testing |
| CRAN checks: |
graphpcor results |