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Merge pull request #218 from jakobrunge/developer
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typos in docstring
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jakobrunge authored May 16, 2022
2 parents 2f65b31 + 9a5bce9 commit 092b7cd
Showing 1 changed file with 9 additions and 9 deletions.
18 changes: 9 additions & 9 deletions tigramite/pcmci.py
Original file line number Diff line number Diff line change
Expand Up @@ -1064,9 +1064,9 @@ def _run_mci_or_variants(self,
alpha_level : float, optional (default: 0.05)
Significance level at which the p_matrix is thresholded to
get graph.
fdr_method : str, optional (default: 'fdr_bh')
fdr_method : str, optional (default: 'none')
Correction method, currently implemented is Benjamini-Hochberg
False Discovery Rate method.
False Discovery Rate method ('fdr_bh').
Returns
-------
Expand Down Expand Up @@ -1218,9 +1218,9 @@ def run_mci(self,
alpha_level : float, optional (default: 0.05)
Significance level at which the p_matrix is thresholded to
get graph.
fdr_method : str, optional (default: 'fdr_bh')
fdr_method : str, optional (default: 'none')
Correction method, currently implemented is Benjamini-Hochberg
False Discovery Rate method.
False Discovery Rate method ('fdr_bh').
Returns
-------
Expand Down Expand Up @@ -1287,9 +1287,9 @@ def get_lagged_dependencies(self,
alpha_level : float, optional (default: 0.05)
Significance level at which the p_matrix is thresholded to
get graph.
fdr_method : str, optional (default: 'fdr_bh')
fdr_method : str, optional (default: 'none')
Correction method, currently implemented is Benjamini-Hochberg
False Discovery Rate method.
False Discovery Rate method ('fdr_bh').
Returns
-------
Expand Down Expand Up @@ -1354,9 +1354,9 @@ def run_fullci(self,
alpha_level : float, optional (default: 0.05)
Significance level at which the p_matrix is thresholded to
get graph.
fdr_method : str, optional (default: 'fdr_bh')
fdr_method : str, optional (default: 'none')
Correction method, currently implemented is Benjamini-Hochberg
False Discovery Rate method.
False Discovery Rate method ('fdr_bh').
Returns
-------
Expand Down Expand Up @@ -1526,7 +1526,7 @@ def _ecdf(x):
else:
# Create a mask for these values
mask = np.ones((N, N, tau_max_plusone), dtype='bool')
# Ignore values from autocorrelation indices
# Ignore values from lag-zero 'autocorrelation' indices
mask[range(N), range(N), 0] = False
# Exclude all contemporaneous values if requested
if exclude_contemporaneous:
Expand Down

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