diff --git a/yli/__init__.py b/yli/__init__.py
index 3ce936f..e92de2e 100644
--- a/yli/__init__.py
+++ b/yli/__init__.py
@@ -14,7 +14,7 @@
 #   You should have received a copy of the GNU Affero General Public License
 #   along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-from .bayes_factors import BayesFactor, bayesfactor_afbf
+from .bayes_factors import bayesfactor_afbf
 from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
 from .fs import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
 from .regress import PenalisedLogit, regress, vif
diff --git a/yli/bayes_factors.py b/yli/bayes_factors.py
index c47ebdf..ed3cfa4 100644
--- a/yli/bayes_factors.py
+++ b/yli/bayes_factors.py
@@ -15,9 +15,7 @@
 #   along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 class BayesFactor:
-	"""
-	A Bayes factor
-	"""
+	"""A Bayes factor"""
 	
 	def __init__(self, factor, num_symbol, num_desc, denom_symbol, denom_desc):
 		self.factor = factor
diff --git a/yli/distributions.py b/yli/distributions.py
index 0c8312a..8af37b3 100644
--- a/yli/distributions.py
+++ b/yli/distributions.py
@@ -284,9 +284,7 @@ transformed_dist = transformed_gen(name='transformed')
 ConfidenceInterval = collections.namedtuple('ConfidenceInterval', ['lower', 'upper'])
 
 def hdi(distribution, level=0.95):
-	"""
-	Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)
-	"""
+	"""Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)"""
 	
 	# For a given lower limit, we can compute the corresponding 95% interval
 	def interval_width(lower):
diff --git a/yli/sig_tests.py b/yli/sig_tests.py
index e6b12b2..dba9cd1 100644
--- a/yli/sig_tests.py
+++ b/yli/sig_tests.py
@@ -188,9 +188,7 @@ class PearsonChiSquaredResult:
 				self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
 
 def chi2(df, dep, ind, *, nan_policy='warn'):
-	"""
-	Perform a Pearson chi-squared test
-	"""
+	"""Perform a Pearson chi-squared test"""
 	
 	# Check for/clean NaNs
 	df = check_nan(df[[ind, dep]], nan_policy)
@@ -209,7 +207,7 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
 	
 	if ct.shape == (2,2):
 		# 2x2 table
-		# Use statsmodels to get OR andRR
+		# Use statsmodels to get OR and RR
 		
 		smct = sm.stats.Table2x2(np.flip(ct.to_numpy()), shift_zeros=False)
 		result = smct.test_nominal_association()