From 646d73964805d8c76c2a0522335afeb190e5386c Mon Sep 17 00:00:00 2001
From: "Benjamin T. Vincent" <inferencelab@gmail.com>
Date: Tue, 17 Sep 2024 08:46:46 +0100
Subject: [PATCH 1/3] fix SyntaxWarning by making some docstrings raw

---
 causalpy/pymc_models.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index 39d26025..06eab620 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -184,7 +184,7 @@ def print_row(
 
 
 class LinearRegression(PyMCModel):
-    """
+    r"""
     Custom PyMC model for linear regression.
 
     Defines the PyMC model
@@ -230,7 +230,7 @@ def build_model(self, X, y, coords):
 
 
 class WeightedSumFitter(PyMCModel):
-    """
+    r"""
     Used for synthetic control experiments.
 
     Defines the PyMC model:
@@ -423,7 +423,7 @@ def fit(self, X, Z, y, t, coords, priors, ppc_sampler=None):
 
 
 class PropensityScore(PyMCModel):
-    """
+    r"""
     Custom PyMC model for inverse propensity score models
 
     .. note:

From 9f5c23a740d40b17b33cec6879d9a9883600b061 Mon Sep 17 00:00:00 2001
From: "Benjamin T. Vincent" <inferencelab@gmail.com>
Date: Wed, 9 Oct 2024 13:12:42 +0100
Subject: [PATCH 2/3] \\beta -> \beta

---
 causalpy/pymc_models.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index 06eab620..bd669215 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -190,11 +190,11 @@ class LinearRegression(PyMCModel):
     Defines the PyMC model
 
     .. math::
-        \\beta &\sim \mathrm{Normal}(0, 50)
+        \beta &\sim \mathrm{Normal}(0, 50)
 
         \sigma &\sim \mathrm{HalfNormal}(1)
 
-        \mu &= X * \\beta
+        \mu &= X * \beta
 
         y &\sim \mathrm{Normal}(\mu, \sigma)
 
@@ -239,9 +239,9 @@ class WeightedSumFitter(PyMCModel):
 
         \sigma &\sim \mathrm{HalfNormal}(1)
 
-        \\beta &\sim \mathrm{Dirichlet}(1,...,1)
+        \beta &\sim \mathrm{Dirichlet}(1,...,1)
 
-        \mu &= X * \\beta
+        \mu &= X * \beta
 
         y &\sim \mathrm{Normal}(\mu, \sigma)
 
@@ -433,11 +433,11 @@ class PropensityScore(PyMCModel):
     Defines the PyMC model
 
     .. math::
-        \\beta &\sim \mathrm{Normal}(0, 1)
+        \beta &\sim \mathrm{Normal}(0, 1)
 
         \sigma &\sim \mathrm{HalfNormal}(1)
 
-        \mu &= X * \\beta
+        \mu &= X * \beta
 
         p &= logit^{-1}(mu)
 

From 553cf535c8066156973b3533210f095360ab5b3f Mon Sep 17 00:00:00 2001
From: "Benjamin T. Vincent" <inferencelab@gmail.com>
Date: Wed, 9 Oct 2024 13:17:54 +0100
Subject: [PATCH 3/3] fix up LaTeX in docstrings

---
 causalpy/pymc_models.py | 35 ++++++++++++-----------------------
 1 file changed, 12 insertions(+), 23 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index bd669215..bbd93110 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -190,13 +190,10 @@ class LinearRegression(PyMCModel):
     Defines the PyMC model
 
     .. math::
-        \beta &\sim \mathrm{Normal}(0, 50)
-
-        \sigma &\sim \mathrm{HalfNormal}(1)
-
-        \mu &= X * \beta
-
-        y &\sim \mathrm{Normal}(\mu, \sigma)
+        \beta &\sim \mathrm{Normal}(0, 50) \\
+        \sigma &\sim \mathrm{HalfNormal}(1) \\
+        \mu &= X \cdot \beta \\
+        y &\sim \mathrm{Normal}(\mu, \sigma) \\
 
     Example
     --------
@@ -236,14 +233,10 @@ class WeightedSumFitter(PyMCModel):
     Defines the PyMC model:
 
     .. math::
-
-        \sigma &\sim \mathrm{HalfNormal}(1)
-
-        \beta &\sim \mathrm{Dirichlet}(1,...,1)
-
-        \mu &= X * \beta
-
-        y &\sim \mathrm{Normal}(\mu, \sigma)
+        \sigma &\sim \mathrm{HalfNormal}(1) \\
+        \beta &\sim \mathrm{Dirichlet}(1,...,1) \\
+        \mu &= X \cdot \beta \\
+        y &\sim \mathrm{Normal}(\mu, \sigma) \\
 
     Example
     --------
@@ -433,14 +426,10 @@ class PropensityScore(PyMCModel):
     Defines the PyMC model
 
     .. math::
-        \beta &\sim \mathrm{Normal}(0, 1)
-
-        \sigma &\sim \mathrm{HalfNormal}(1)
-
-        \mu &= X * \beta
-
-        p &= logit^{-1}(mu)
-
+        \beta &\sim \mathrm{Normal}(0, 1) \\
+        \sigma &\sim \mathrm{HalfNormal}(1) \\
+        \mu &= X \cdot \beta \\
+        p &= \text{logit}^{-1}(\mu) \\
         t &\sim \mathrm{Bernoulli}(p)
 
     Example