Models for Correlation Matrices Based on Graphs R package graphpcor version 0.1.25 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:10.1007/s10260-025-00788-y>. 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. Software Depends: R (>= 4.3), Matrix, INLAtools (> 0.1.3), numDeriv Imports: methods, stats, utils, igraph Suggests: knitr, INLA (>= 24.02.09) Elias Teixeira Krainski <elias.krainski@kaust.edu.sa> Comprehensive R Archive Network (CRAN) Elias Teixeira Krainski [cre, aut, cph] (ORCID: <https://orcid.org/0000-0002-7063-2615>), Denis Rustand [aut, cph] (ORCID: <https://orcid.org/0000-0001-9708-5220>), Anna Freni-Sterrantino [aut, cph] (ORCID: <https://orcid.org/0000-0002-6602-6209>), Janet van Niekerk [aut, cph] (ORCID: <https://orcid.org/0000-0002-4334-2057>), Haavard Rue’ [aut] (ORCID: <https://orcid.org/0000-0002-0222-1881>) GPL (>= 2) 2026-05-07 application/tgz https://CRAN.R-project.org/package=graphpcor doi:10.32614/CRAN.package.graphpcor