v1.9.0

New Features

• Added option measure=pwling_fast to accelerate DirectLiNGAM by parallelizing causal order on GPU with CUDA.
• Added tools.bootstrap_with_imputation function to interpolate missing data by Multiple Imputation Method and perform causal discovery by bootstrap method for datasets containing missing value.
• Added HighDimDirectLiNGAM class, a DirectLiNGAM algorithm suitable for high-dimensional data.

Code Fixes

• Fixed the calculation of the total effect in LongitudinalLiNGAM. (#136)
• Changed from using AdaptiveLasso to LinerRegression to calculate the total effect. (#137)
• Fixed the calculation method of regression coefficients when calculating residuals in DirectLiNGAM. (#141)

v1.8.3

New Features

• Added utils.evaluate_model_fit function to calculate fit indices.
• Added ind_corr option to use F-correlation to determine independence in BottomUpParceLiNGAM, RCD, MultiGroupRCD, and CAMUV algorithms.
• Added utils.calculate_distance_from_root_nodes function to calculate the shortest distance from the root variable to other variables based on the structure of causal graphs.
• Added utils.calculate_total_effect function to calculate causal effects based on the structure of causal graphs.

Code Fixes

• Fixed the calculation of total effect in LongitudinalLiNGAM.estimate_total_effect. (#122)
• Changed the argument name of sklearn.OneHotEncoder used in experimental.CausalDataGenerator from sparse to sparse_output. This fix requires scikit-learn v1.2 or higher. (#123)

v1.8.2

New Features

• Added LiNA and MDLiNA algorithm.

Code Fixes

• Fixed standardization in lingam.utils.predict_adaptive_lasso function without normalize option in sklearn.linear_model.LassoLarsIC.
• Changed to use lingam.utils.predict_adaptive_lasso function in VARLiNGAM and VARMALiNGAM.
• Changed prune option to True by default in VARLiNGAM and VARMALiNGAM. Additionally, changed the description that this option is for the causal effect of lag.

v1.8.1

New Features

• Added utils.f_correlation function to calculate F-correlation.
• Added utils.visualize_nonlinear_causal_effect function to plot nonlinear causal effects.
• Added an option for LiM to perform the local search or not.
• Added resampled_indices_ property to BootstrapResult.

Code Fixes

• Fixed a bug that labels specified in the utlis.make_dot_highlight function were not reflected in graphs.
• Modified MultiGroupDirectLiNGAM algorithm to allow specifying datasets of different sizes.
• Removed LiNA and MDLiNA algorithm from this package. Independent as a new lina package. https://github.com/cdt15/lina

Installation

• Changed supported Python version to 3.8 or higher.
• Eliminated the fixed numpy version.

v1.8.0

New Features

• Added MultiGroupRCD algorithm, RCD for multiple datasets.
• Add extract_ancestors method to lingam.utils.
• Modified hsic.py to speed up independence test HSIC.
• Added to utils.make_dot the ability to highlight paths between variables in a causal graph.
• Added utils.make_dot_hightlight method to highlight ancestors and descendants of a specified variable in a causal graph.

Experimental features

• Added CausalDataGenerator tool to lingam.experimental.

Code Fixes

• Fixed VAR trend argument used in VARLiNGAM (#86)

Documentation

• Minor text correction on causal_effect.rst

v1.7.1

New Features

• Added CausalBasedSimulator that can generate virtual data using causal graphs.

Code Fixes

• Fixed #58, #70

Tests

• Changed supported python versions

Documentation

• Enhanced tutorials
• Added link to tutorial slides

v1.7.0

New Features

• Added LiM algorithm
• Added CAM-UV algorithm

Code Fixes

• Fixed typos (#50)
• Fixed #44
• Fixed #45
• Fixed #53

v1.6.0

New Features

• Added LiNA and MDLiNA algorithm
• Added RESIT algorithm

v1.5.5

New Features

• Added get_paths method to the result of bootstrapping that searches all paths between specified variables and outputs the bootstrap probability for each path in VARLiNGAM, VARMALiNGAM and LongitudinalLiNGAM

Code Fixes

• Changed the timing for creating the partial orders when applying prior knowledge in DirectLiNGAM
• Fixed that prior knowledge is not applied when there are duplicate no_paths (#34)

v1.5.4

New Features

• Added get_paths method to BootstrapResult that searches all paths between specified variables and outputs the bootstrap probability for each path in ICALiNGAM, DirectLiNGAM, MultiGroupDirectLiNGAM, BottomUpParceLiNGAM and RCD