Lingam

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)

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)

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.

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.

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

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