v0.2.0

• Update dependencies, removed old pinned versions for decorator, sphinx, jinja

New features:

• Elementwise regularization parameter for single Graphical Lasso. Can be used by passing lambda1_mask in reg_params (should be a array of nonnegative numbers that has same dimension as covariance matrix S).

Enhancements:

• New example in example gallery, showing the usage of Functional Graphical Lasso: https://gglasso.readthedocs.io/en/latest/auto_examples/plot_fsgl_example.html#sphx-glr-auto-examples-plot-fsgl-example-py

v0.1.10

Minor Changes:

• remove usage of np.float as this is deprecated. Instead use np.float16,np.float32,...
• update 3d plot to work with newer versions of matplotlib (gca was deprecated)
• Update Readthedocs YAML to match the new file requirements (include build section)

v0.1.9

Reverts the change on default rescaling behaviour: in glasso_problem, by default the solution gets rescaled to covariances. This has the effect that resolving with the regularization parameters found during model selection and with the unscaled input, it is not ensured that you obtain the same solution (as the model selection takes place for the scaled input).

v0.1.8

New features:

• solver for Functional Graphical Lasso
• experimental: thresholding in grid searches (not yet included in glasso_problem)

Minor changes:

• changes in defaults for glasso_problem
• no rescaling by default
• store_all flag allows access to all solutions on the grid

v0.1.7

This release is the packages version after review at JOSS.