graphpcor
Models for Correlation Matrices Based on Graphs
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.24 |
rolling source/ R- | graphpcor_0.1.24.tar.gz |
553.2 KiB |
0.1.24 |
rolling linux/jammy R-4.5 | graphpcor_0.1.24.tar.gz |
713.5 KiB |
0.1.24 |
rolling linux/noble R-4.5 | graphpcor_0.1.24.tar.gz |
713.6 KiB |
0.1.24 |
latest source/ R- | graphpcor_0.1.24.tar.gz |
553.2 KiB |
0.1.24 |
latest linux/jammy R-4.5 | graphpcor_0.1.24.tar.gz |
713.5 KiB |
0.1.24 |
latest linux/noble R-4.5 | graphpcor_0.1.24.tar.gz |
713.6 KiB |
0.1.24 |
2026-04-23 source/ R- | graphpcor_0.1.24.tar.gz |
553.2 KiB |
0.1.24 |
2026-04-09 windows/windows R-4.5 | graphpcor_0.1.24.zip |
716.2 KiB |
0.1.11 |
2025-04-20 source/ R- | graphpcor_0.1.11.tar.gz |
55.9 KiB |