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graphpcor

Models for Correlation Matrices Based on Graphs

v0.1.24 · Mar 23, 2026 · GPL (>= 2)

Description

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.

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ERROR 6 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Mar 23, 2026
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Dependency Network

Dependencies Reverse dependencies Matrix igraph INLAtools numDeriv graphpcor

Version History

new 0.1.24 Mar 23, 2026