GGMnonreg-package: GGMnonreg: Non-Regularized Gaussian Graphical Models

In donaldRwilliams/GGMnonreg: Non-Regularized Gaussian Graphical Models

Description

The goal of GGMnonreg is to estimate non-regularized graphical models. Note that the title is a bit of a misnomer, in that Ising and mixed graphical models are also supported. Graphical modeling is quite common in fields with wide data, that is, when there are more variables than observations. Accordingly, many regularization-based approaches have been developed for those kinds of data. There are key drawbacks of regularization when the goal is inference, including, but not limited to, the fact that obtaining a valid measure of parameter uncertainty is very (very) difficult.

More recently, graphical modeling has emerged in psychology, where the data are typically long or low-dimensional \insertCitewilliams_rethinking,williams2019nonregularizedGGMnonreg. The primary purpose of GGMnonreg is to provide methods specifically for low-dimensional data

Supported Models

• Gaussian graphical model. The following data types are supported.

  • Gaussian

  • Ordinal

  • Binary

• Ising model

• Mixed graphical model

Additional Methods

• Expected network replicability \insertCitewilliams2020learningGGMnonreg

• Compare Gaussian graphical models

• Measure of uncertainty \insertCitewilliams_2021_confGGMnonreg

• Edge inclusion "probabilities"

• Network visualization

• Constrained precision matrix (the network, given an assumed graph)

• Predictability (variance explained)

References

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donaldRwilliams/GGMnonreg documentation built on Nov. 13, 2021, 9:57 a.m.