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 |
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 |
Reference manual: | graphpcor.pdf |
Package source: | graphpcor_0.1.12.tar.gz |
Windows binaries: | r-devel: graphpcor_0.1.12.zip, r-release: graphpcor_0.1.12.zip, r-oldrel: graphpcor_0.1.12.zip |
macOS binaries: | r-release (arm64): graphpcor_0.1.12.tgz, r-oldrel (arm64): graphpcor_0.1.12.tgz, r-release (x86_64): graphpcor_0.1.12.tgz, r-oldrel (x86_64): graphpcor_0.1.12.tgz |
Old sources: | graphpcor archive |
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