Tutorials overview

The following tutorials demonstrate how to use the various features of SpectraNorm. Each tutorial focuses on a specific aspect of the package, with step-by-step instructions and example code.

The tutorials begin with the most basic functionality, such as fitting a univariate normative model and visualizing the results. Later tutorials cover more advanced topics, including how to construct graph spectral eigenbases and apply spectral normative modeling to characterize graph-defined data. Neuroimaging-specific examples are also included, such as applying spectral normative modeling to cortical thickness data from an imaging study.

The tutorials are designed to be accessible to users with varying levels of programming and normative modeling experience.

Available tutorials

• A simple univariate normative model: How to fit a simple univariate normative model to data and evaluation/visualize the results using the package's various utilities.

• Constructing graph spectral eigenbases: How to construct graph spectral eigenbases and use them to represent high-dimensional data defined on graphs.

• An example spectral normative model: How to fit a spectral normative model to graph-defined data, and how to interpret and evaluate the results.