# Announcement: GraSPy 0.0.2¶

We're happy to announce the release of GraSPy 0.0.2! GraSPy is a Python package for understanding the properties of random graphs that arise from modern datasets, such as social networks and brain networks.

For more information, please visit our website and our tutorials.

## Highlights¶

This release is the result of 3 months of work with over 16 pull requests by 5 contributors. Highlights include:

- Nonparametric hypothesis testing method for testing two non-vertex matched graphs.
- Plotting updates to
`pairplot`

,`gridplot`

and`heatmaps`

. - Sampling degree-correlcted stochatic block models (DC-SBM).
`import_edgelist`

function for importing single or multiple edgelists.- Enforcing
`Black`

formatting for the package.

## Improvements¶

- Embedding methods are now fully sklearn-compliant. This is tested via
`check_estimator`

function in sklearn. `gridplot`

and`heatmap`

can now plot hierchical labels.- New Laplacian computing method ('R-DAD') by adding a constant to the diagonal degree matrix.
- Semiparametric testing only checks for largest connected component (LCC) in the intial embeddings.
- Various bug fixes.
- Various tutorial latex fixes.
- Various documentation clarifications.
- More consistent documentation.

## API Changes¶

`check_lcc`

argument in`AdjacencySpectralEmbed`

,`LaplacianSpectralEmbed`

, and`OmnibusEmbed`

classes, which checks if input graph(s) are fully connected when`check_lcc`

is True.`gridplot`

and`heatmap`

now have a`inner_hier_labels`

and`outer_hier_labels`

, which are used for hierarchical labeling of nodes.`to_laplace`

function now has`regularizer`

arg for when`form`

is 'R-DAD'.`sbm`

function now has`dc`

and`dc_kws`

arguments for sampling SBM with degree-correction.

## Deprecations¶

None.