some ReadTheDocs fixes

pzivich · Jul 17, 2019 · 4b5b4ed · 4b5b4ed
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Showing 2 changed files with 6 additions and 4 deletions.
diff --git a/zepid/calc/utils.py b/zepid/calc/utils.py
@@ -1323,10 +1323,10 @@ def s_value(pvalue):
     hypothesis. For an example, a S-value of 5.1 is no more surprising than seeing heads for 5 fair coin tosses. The
     S-value should be rounded down in the interpretation
 
-    Notes
-    -----
+    Note
+    ----
     S-values do NOT have a significant cut-point. Rather this transformation is to help build intuition what information
-    a p-values is providing and the corresponding 'surprisal'
+    a p-values is providing and the corresponding 'surprisal' of a result
 
     Parameters
     ----------

diff --git a/zepid/causal/ipw/IPTW.py b/zepid/causal/ipw/IPTW.py
@@ -107,6 +107,7 @@ class IPTW:
     >>> ipt.summary()
 
     Stabilized IPTW with IPCW
+
     >>> ipt = IPTW(df, treatment='art', outcome='dead')
     >>> ipt.treatment_model('male + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
     >>> ipt.missing_model('art + male + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0')
@@ -115,6 +116,7 @@ class IPTW:
     >>> ipt.summary()
 
     Stabilized IPTW with effect measure modifier
+
     >>> ipt = IPTW(df, treatment='art', outcome='dead')
     >>> ipt.treatment_model('male + age_rs1 + age_rs2 + cd40 + cd4_rs1 + cd4_rs2 + dvl0', model_numerator='male')
     >>> ipt.marginal_structural_model('art + male + art:male')
@@ -706,7 +708,7 @@ class StochasticIPTW:
 
     .. math::
 
-        \pi_i = \frac{\bar{\Pr}(A=a|L)}{\Pr(A=a|L)}
+        \pi_i = \frac{\overline{\Pr}(A=a|L)}{\Pr(A=a|L)}
 
     where :math:`\bar{\Pr}` is the new probability of treatment under the proposed stochastic treatment. This
     probability can be unconditional (everyone treated at some constant percent) or it can be conditional on observed