updated docs

kunwuz · kunwuz · commit e88630a32e00 · 2025-03-04T02:56:13.000-05:00
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
@@ -75,11 +75,11 @@ Contributors
 - Wai-Yin Lam: :ref:`PC <pc>`.
 - Biwei Huang: :ref:`CD-NOD <cdnod>`.
 - Ignavier Ng, Yujia Zheng: :ref:`Exact search <exactsearch>`.
-- Bryan Andrews, Joseph Ramsey: :ref:`GRaSP <GRaSP>`.
+- Bryan Andrews, Joseph Ramsey: :ref:`GRaSP <GRaSP>`, :ref:`BOSS <BOSS>`.
 - Joseph Ramsey, Wei Chen, Zhiyi Huang: :ref:`Evaluations <evaluation>`.
 
 
-**Quality control**: Yewen Fan, Haoyue Dai, Yujia Zheng, Ignavier Ng, Xiangchen Song
+**Quality control**: Yewen Fan, Zhiyi Huang, Haoyue Dai, Yujia Zheng, Ignavier Ng, Xiangchen Song
 
 Citation
 ^^^^^^^^^^^^
diff --git a/docs/source/independence_tests_index/index.rst b/docs/source/independence_tests_index/index.rst
@@ -4,6 +4,8 @@ In this section, we would like to introduce (conditional) independence tests in
 Missing-value Fisher-z test, Chi-Square test, Kernel-based conditional independence (KCI) test and independence test [2]_,
 and G-Square test [3]_.
 
+(For more efficient nonparametric test, you may try `FastKCI and RCIT  <https://github.com/py-why/causal-learn/pull/202>`_. Both implementations are still preliminary and there might be some issues.)
+
 
 Contents:
 
diff --git a/docs/source/search_methods_index/Constraint-based causal discovery methods/CDNOD.rst b/docs/source/search_methods_index/Constraint-based causal discovery methods/CDNOD.rst
@@ -51,6 +51,9 @@ and n_features is the number of features.
        - ":ref:`kci <Kernel-based conditional independence (KCI) test and independence test>`": kernel-based conditional independence test. (As a kernel method, its complexity is cubic in the sample size, so it might be slow if the same size is not small.)
        - ":ref:`mv_fisherz <Missing-value Fisher-z test>`": Missing-value Fisher's Z conditional independence test.
 
+(For more efficient nonparametric test, you may try `FastKCI and RCIT  <https://github.com/py-why/causal-learn/pull/202>`_. Both implementations are still preliminary and there might be some issues.)
+
+
 **stable**: run stabilized skeleton discovery [3]_ if True. Default: True.
 
 **uc_rule**: how unshielded colliders are oriented. Default: 0.
diff --git a/docs/source/search_methods_index/Constraint-based causal discovery methods/FCI.rst b/docs/source/search_methods_index/Constraint-based causal discovery methods/FCI.rst
@@ -43,6 +43,9 @@ and n_features is the number of features.
        - ":ref:`kci <Kernel-based conditional independence (KCI) test and independence test>`": kernel-based conditional independence test. (As a kernel method, its complexity is cubic in the sample size, so it might be slow if the same size is not small.)
        - ":ref:`mv_fisherz <Missing-value Fisher-z test>`": Missing-value Fisher's Z conditional independence test.
 
+(For more efficient nonparametric test, you may try `FastKCI and RCIT  <https://github.com/py-why/causal-learn/pull/202>`_. Both implementations are still preliminary and there might be some issues.)
+
+
 **alpha**: Significance level of individual partial correlation tests. Default: 0.05.
 
 **depth**: The depth for the fast adjacency search, or -1 if unlimited. Default: -1.
diff --git a/docs/source/search_methods_index/Constraint-based causal discovery methods/PC.rst b/docs/source/search_methods_index/Constraint-based causal discovery methods/PC.rst
@@ -81,6 +81,9 @@ and n_features is the number of features.
        - ":ref:`kci <Kernel-based conditional independence (KCI) test and independence test>`": kernel-based conditional independence test. (As a kernel method, its complexity is cubic in the sample size, so it might be slow if the same size is not small.)
        - ":ref:`mv_fisherz <Missing-value Fisher-z test>`": Missing-value Fisher's Z conditional independence test.
 
+(For more efficient nonparametric test, you may try `FastKCI and RCIT  <https://github.com/py-why/causal-learn/pull/202>`_. Both implementations are still preliminary and there might be some issues.)
+
+
 **stable**: run stabilized skeleton discovery [4]_ if True. Default: True.
 
 **uc_rule**: how unshielded colliders are oriented. Default: 0.
diff --git a/docs/source/search_methods_index/Permutation-based causal discovery methods/BOSS.rst b/docs/source/search_methods_index/Permutation-based causal discovery methods/BOSS.rst
diff --git a/docs/source/search_methods_index/Permutation-based causal discovery methods/index.rst b/docs/source/search_methods_index/Permutation-based causal discovery methods/index.rst
@@ -9,6 +9,6 @@ Contents:
     :maxdepth: 2
 
     GRaSP
-    boss
+    BOSS
 
 .. [1] Lam, W. Y., Andrews, B., & Ramsey, J. (2022, February). Greedy Relaxations of the Sparsest Permutation Algorithm. In The 38th Conference on Uncertainty in Artificial Intelligence.
diff --git a/docs/source/search_methods_index/Score-based causal discovery methods/GES.rst b/docs/source/search_methods_index/Score-based causal discovery methods/GES.rst
@@ -49,9 +49,9 @@ and n_features is the number of features.
 **score_func**: The score function you would like to use, including (see :ref:`score_functions`.). Default: 'local_score_BIC'.
               - ":ref:`local_score_BIC <BIC score>`": BIC score [3]_.
               - ":ref:`local_score_BDeu <BDeu score>`": BDeu score [4]_.
-              - ":ref:`local_score_cv_general <Generalized score with cross validation>`": Generalized score with cross validation for data with single-dimensional variables [2]_.
+              - ":ref:`local_score_CV_general <Generalized score with cross validation>`": Generalized score with cross validation for data with single-dimensional variables [2]_.
               - ":ref:`local_score_marginal_general <Generalized score with marginal likelihood>`": Generalized score with marginal likelihood for data with single-dimensional variables [2]_.
-              - ":ref:`local_score_cv_multi <Generalized score with cross validation>`": Generalized score with cross validation for data with multi-dimensional variables [2]_.
+              - ":ref:`local_score_CV_multi <Generalized score with cross validation>`": Generalized score with cross validation for data with multi-dimensional variables [2]_.
               - ":ref:`local_score_marginal_multi <Generalized score with marginal likelihood>`": Generalized score with marginal likelihood for data with multi-dimensional variables [2]_.
 
 **maxP**: Allowed maximum number of parents when searching the graph. Default: None.
