v2.12.0

• Added pyagrum module

v2.11.5

Minor fixes in the docs etc.

v2.11.0

• This version introduces the bips_tpc module, based on the temporal PC (tPC) and the micd package. It enables causal discovery through the PC algorithm allowing for Edge constraints, mixed data, and missing data handling.

v2.10.1

Some bug fixes and updates to the docs.

v2.10.0

New capabilities to handle missing data problems

• Sample data through mvpc_gen_data module
• Causal discovery using the mvpc module

v2.9.0

Three major new features:

• The ability to incorporate background knowledge in terms of edge Constraints.
• The ability to specify multiple benchmark setups in the same config file.
• A new module gcastle_dag, for generating DAGs using the gCastle package

v2.8.0

• This version introduces the new graph_estimation module which will replace the graph_plots module. It enables conversions to other graph types and the possibility to specify which features.

v2.7.1

• New algorithm modules from the huge package for estimating Gaussian graphical models: huge_glasso, huge_mb, and huge_tiger.
• Updated trilearn to version 2.0.5
• Updated docs

v2.6.1

• Mainly bug fixes, e.g. in the trilearn_pgibbs module

v2.6.0

• Added the GrUES algorithm (Alex Markham).
• Changed all occurrences of replicate to seed in the code. This will unfortunately trigger a rerun of existing jobs as the scripts are changed.
• Added F1 score based on the pattern graph (v-structure kept while the other edges are undirected) of a DAG.