fix doc string issues

neurodata · Sep 10, 2024 · 767c106 · 767c106
1 parent a753cb1
commit 767c106
Show file tree

Hide file tree

Showing 4 changed files with 12 additions and 9 deletions.
diff --git a/docs/api/index.rst b/docs/api/index.rst
@@ -99,8 +99,8 @@ Conditional Independence
     :toctree: generated/
 
     ConditionalDcorr
-    PartialCorr
     PartialDcorr
+    PartialCorr
     FCIT
     KCI
 

diff --git a/hyppo/conditional/pcorr.py b/hyppo/conditional/pcorr.py
@@ -46,7 +46,7 @@ class PartialCorr(ConditionalIndependenceTest):
     -----
     The statistic is computed as follows:
 
-    ..math::
+    .. math::
         r_{x, y ; z} = \frac{\rho_{xy} - \rho_{xz} \rho_{yz}}{\sqrt{(1 - \rho_{xz}^2)(1 - \rho_{yz}^2)}}
 
     where :math:`\rho_{xy}` is the Pearson correlation coefficient between :math:`x` and

diff --git a/hyppo/conditional/pdcorr.py b/hyppo/conditional/pdcorr.py
@@ -59,20 +59,23 @@ class PartialDcorr(ConditionalIndependenceTest):
     partial distance covariance is defined as
 
     .. math::
-        \mathrm{PDcov}_n (x, y; z) &= \frac{1}{n(n-3)} \sum_{i\neq j}^n \left(P_{z^\perp}(x)\right)_{i,j} \left(P_{z^\perp}(y)\right)_{i,j}
+        \mathrm{PDcov}_n (x, y; z) = \frac{1}{n(n-3)} \sum_{i\neq j}^n \left(P_{z^\perp}(x)\right)_{i,j} \left(P_{z^\perp}(y)\right)_{i,j}
+
     where
     .. math::
-        P_{z^\perp}(x) &= C^x - \frac{(C^x\cdot C^z)}{ C^z \cdot C^z) C^z
+        P_{z^\perp}(x) = C^x - \frac{(C^x\cdot C^z)}{ C^z \cdot C^z) C^z
+
     is the orthogonal proejction of :math:`C^x` onto the subspace orthogonal to :math:`C^z`.
     The partial distance correlation is defined as
 
     .. math::
-        \mathrm{PDcorr}_n (x, y; z) &= \frac{P_{z^\perp}(x)\cdot P_{z^\perp}(y)}{\abs{P_{z^\perp}(x)}\abs{P_{z^\perp}(y)}}
+        \mathrm{PDcorr}_n (x, y; z) = \frac{P_{z^\perp}(x)\cdot P_{z^\perp}(y)}{\abs{P_{z^\perp}(x)}\abs{P_{z^\perp}(y)}}
 
     Equivalently, the partial distance correlation can be also defined as
 
     .. math::
-        \mathrm{CDcorr}_n (x, y; z) &=  \frac{R_{xy} - R_{xz} R_{yz}}{\sqrt{(1 - R_{xz}^2)(1 - R_{yz}^2)}}
+        \mathrm{CDcorr}_n (x, y; z) =  \frac{R_{xy} - R_{xz} R_{yz}}{\sqrt{(1 - R_{xz}^2)(1 - R_{yz}^2)}}
+
     where :math:`R_{xy}` is the unbiased distance correlation between :math:`x` and :math:`y`.
 
     References
@@ -199,7 +202,7 @@ def test(
 
 
 @jit(nopython=True, cache=True)
-def _pdcov(distx, disty, distz): # pragma: no cover
+def _pdcov(distx, disty, distz):  # pragma: no cover
     """Calculate the PDcov test statistic"""
     N = distx.shape[0]
     denom = N * (N - 3)
@@ -223,7 +226,7 @@ def _pdcov(distx, disty, distz): # pragma: no cover
 
 
 @jit(nopython=True, cache=True)
-def _pdcorr(distx, disty, distz): # pragma: no cover
+def _pdcorr(distx, disty, distz):  # pragma: no cover
     """Calculate the PDcorr test statistic"""
 
     distx = _center_distmat(distx, bias=False)

diff --git a/hyppo/time_series/corrx.py b/hyppo/time_series/corrx.py
@@ -34,7 +34,7 @@ class LjungBox(TimeSeriesTest):
         \mathrm{Ljung-Box}_n (x, y) =  n(n+2)\sum_{j=1}^M \frac{
             \rho^2(x[j:n], y[0:(n-j)])}{n-j}
 
-    where $\rho$ is the Pearson correlation coefficient.
+    where :math:`\rho` is the Pearson correlation coefficient.
     The p-value returned is calculated either via chi-squared distribution or
     using a permutation test.