Pgmpy

Added
1. Option to specify the node names in random model generation methods.
2. ExpertInLoop.estimate method now accepts any LLM model supported by litellm.
3. AIC and BIC structure scoring methods for Gaussian variables and Conditional Gaussian variables.
4. Greedy Equivalence Search algorithm for causal discovery.
5. Causal Discovery algorithms can automatically figure out the data types of the variables.
6. Support for continuous and mixed data types for all causal discovery / structure learning algorithms.
7. Adds LinearGaussianBayesianNetwork and FunctionalBayesianNetwork classes to represent Gaussian and hybrid Bayesian Networks.
8. Add `pgmpy.metrics.SHD` to compute the Structural Hamming Distance between two DAGs.
9. Adds `NoisyORCPD` class to represent NoisyOr models.
10. BayesianNetwork.simulate method can not simulate different types of missing data.
11. BayesianNetwork.predict now predicts any missing value in the dataframe instead of missing columns.
12. Adds continuous example models from bnlearn repository.
13. Adds `ExpertKnowledge` class to specify expert knowledge for structure learning algorithms.
14. Option to initialize DAG and DiscreteBayesianNetwork with adjacency matrix, a dagitty model, or a lavaan model string.
15. Adds method for reading and writing XDSL file format (used by GeNIe).
16. Adds Generalized Covariance Measure (GCM) conditional independence test.

Fixed
1. Fixes bug in `pgmpy.estimators.SEMEstimator`.

Changed
1. Renames `pgmpy.estimators.CITests.ci_pillai` to `pgmpy.estimators.CITests.pillai_trace`.
2. `BayesianNetwork.fit` method moved to `DAG.fit` so that fitting can be done on either model types.
3. All structure scoring methods have been renamed to simplify.

Removed
1. Removes some of the CI test variants of chi-squared test.
2. Removes `pgmpy.factors.continuous.ContinuousFactor` class.
2. Removes discretization methods for ContinuousFactor.
3. `BayesianModel` and `MarkovModel` classes have been removed.
4. `BayesianNetwork` class have been removed. Use `DiscreteBayesianNetwork` instead.

Added
1. Support for returning Belief Propagation messages in Factor Graph BP.
2. Maximum Likelihood Estimator for Junction Tree.
3. Adds a simple discretization method: `pgmpy.utils.discretize`.
4. Two new metrics for model testing: `pgmpy.metrics.implied_cis` and `pgmpy.metrics.fisher_c`.
5. Support for Linear Gaussian Bayesian Networks: estimation, prediction, simulation and random model generation.
7. New mixed data Conditional Independence test based on canonical correlations.
8. New LLM based structure learning / causal discovery algorithm. Also LLM based pairwise variable orientation method.


Fixed
1. Reading and Writing from XBN file format.
2. Documentation for plotting models.
3. Fixes PC algorithm to add disconnected nodes in the final model.
4. Allows `.` in variables names in BIF file format.

Changed
1. Allows `virtual_evidence` parameter in inference methods to accept DiscreteFactor objects.

Added
1. `init_cpds` argument to `ExpecattionMaximiation.get_parameters` to specify initialization values.
2. BeliefPropagation with message passing for Factor Graphs.
3. Marginal Inference for undirected graphs.

Fixed
1. Incompatibality with networkx==3.2.
2. `CausalInference.get_minimal_adjustment_set` to accept string variable names.
3. Bug in EM when latent varaibles are present.
4. `compat_fns.copy` to consider the case when int or float is passed.
5. Fixes issue with `BayesianNetwork.fit_update` when running with CUDA backend.

Changed
1. Documentation Updates
2. Optimizations for Hill Climb Search algorithm.
3. Tests shutdown parallel workers in teardown.
4. Removes the `complete_samples_only` argument from `BaseEstimator.state_counts`.
5. Default number of cores to use changed to 1 for parameter estimation methods.

Added
1. Added support for python 3.11.
2. Adds `DAG.to_graphviz` and `PDAG.to_graphviz` methods to convert model to graphviz objects.
3. Adds pytorch as an alternative backend.
4. Adds unicode support for BIFReader.

Fixed
1. Warnings use a logger instance.
2. Fixes documentation.
3. Fixes variables name arguments for `CausalInference.get_minimal_adjustment_set`

Changed
1. Adds argument to specify samples for ApproxInference.
2. Memory optimizations for computing structure scores.
3. Switches joblib backed to loky.
4. Runtime optimizations for sampling.
5. Runtime optimizations for Variable Elimination.
6. All config variables moved to `pgmpy.global_vars`.

Added
1. BIFReader made compatible with the output of PyAgrum
2. Support for all available CI tests in PC algorithm.
3. References for read/write file formats.

Removed
1. Removes `DAG.to_pdag` method.

Changed
1. Fixes for ApproxInference for DBNs.
2. Make `xml.etree` the default parser instead of using lxml.

Added
1. AIC score metric from score based structure learning.
2. Adds support for NET (HUGIN) file format.
3. Adds argument reindex to `state_counts` method.

Fixed
1. Bug in GibbsSampling when sampling from Bayesian Networks.
2. Fix seed for all simulation methods.
3. Memory leaks when using `lru_cache`.

Changed
1. Caching disabled for computing state name counts during structure learning.
2. Pre-computation for sampling methods are optimized.