updated docs

Author: kunwuz

docs/source/search_methods_index/Constrained-based causal discovery methods/CDNOD.rst

@@ -20,7 +20,8 @@ Usage
 
 Parameters
 -------------------
-**data**: data set (numpy ndarray).
+**data**:  numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples
+and n_features is the number of features.
 
 **c_indx**: time index or domain index that captures the unobserved changing factors.
 

docs/source/search_methods_index/Constrained-based causal discovery methods/FCI.rst

@@ -20,7 +20,8 @@ Usage
 
 Parameters
 -------------------
-**data**: Input data matrix
+**data**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples
+and n_features is the number of features.
 
 **alpha**: Significance level of individual partial correlation tests.
 

docs/source/search_methods_index/Constrained-based causal discovery methods/PC.rst

@@ -23,7 +23,8 @@ Usage
 
 Parameters
 -------------------
-**data**: data set (numpy ndarray).
+**data**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples
+and n_features is the number of features.
 
 **alpha**: desired significance level (float) in (0, 1).
 

docs/source/search_methods_index/Granger causality/LinearGranger.rst

@@ -19,7 +19,8 @@ Usage
 Parameters
 -------------------
 
-**data**: input data (n, d).
+**data**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples
+and n_features is the number of features.
 
 Returns
 -------------------

docs/source/search_methods_index/Hidden causal representation learning/gin.rst

@@ -17,7 +17,8 @@ Usage
 
 Parameters
 -----------------------------------------------------------
-**data**: numpy ndarray. Data set.
+**data**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples
+and n_features is the number of features.
 
 Returns
 -----------------------------------------------------------

docs/source/search_methods_index/Score-based causal discovery methods/GES.rst

@@ -18,7 +18,8 @@ Usage
 
 Parameters
 -------------------
-**X**: Data with T*D dimensions.
+**X**: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples
+and n_features is the number of features.
 
 **score_func**: The score function you would like to use, including (see :ref:`score_functions`.).
               - ":ref:`local_score_BIC <BIC score>`": BIC score [3]_.