Models for Correlation Matrices Based on Graphs
Build an inla.cgeneric to implement a model whose precision has a co...
Build an inla.cgeneric for a graph, see graphpcor()
Build an inla.cgeneric object to implement the LKG prior for the cor...
Build an inla.cgeneric to implement the PC prior, proposed on Simpso...
Build an inla.cgeneric to implement the PC-prior of a precision matr...
Build an cgeneric for treepcor())
Build an inla.cgeneric to implement the Wishart prior for a precisio...
inla.cgeneric class, short cgeneric, to define a `INLA::cgeneric()...
Build the correlation matrix parametrized from the hypershere decompos...
The LKJ density for a correlation matrix
Set a graph whose nodes and edges represent variables and conditional ...
The graphpcor generic method for graphpcor
Evaluate the hessian of the KLD for a graphpcorcorrelation model aro...
Define the is.zero method
The Laplacian of a graph
Functions for the mapping between spherical and Euclidean coordinates.
The prec method
Precision matrix parametrization helper functions.
inla.rgeneric class, short rgeneric, to define a `INLA::rgeneric()...
Set a tree whose nodes represent the two kind of variables: children a...
Define a tree used to model correlation matrices using a shared latent...
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.