Computing the Singular BIC for Multiple Models
Construct a poset of binomial mixture models.
EM-algorithm for latent forests.
One EM-iteration.
Construct a poset of factor analysis models.
Construct a poset of gaussian mixture models.
Generate all non-isomorphic binary trees.
Edges representing the largest model.
Create a covariance matrix.
Return the set data.
Model dimension.
Get model with the given support.
Number of factors for a model.
Get number of leaves.
Number of models.
Number of samples in the set data.
Maximum number of vertices.
Get the phi parameter.
The prior on the models.
Sampling covariance matrix.
Get support for a given model.
Topological ordering of models.
Construct a poset of gaussian latent forest models.
Construct a poset of latent class analysis models.
Learning coefficient
Multivariate gaussian log-likelihood.
Maximum likelihood for data.
Help compute the MLE.
Linear collections of mixture models.
Maximum likelihood estimator.
Parents of a model.
Construct a poset of reduced rank regression models.
sBIC package documentation.
Compute the sBIC.
Set data for the binomial mixture models.
Set data for the factor analysis models.
Set data for the gaussian mixture models.
Set data for the latent forest models.
Set data for the LCA models.
Set data for a model poset.
Set data for the reduced rank regression models.
Set phi parameter.
Computes the sBIC for various singular model collections including: binomial mixtures, factor analysis models, Gaussian mixtures, latent forests, latent class analyses, and reduced rank regressions.