update config

Author: ljchang

_config.yml

@@ -32,7 +32,9 @@ parse:
   myst_enable_extensions:
     - html_image
     - dollarmath
+    - linkify
   myst_url_schemes: [mailto, http, https] # URI schemes that will be recognised as external URLs in Markdown links
+  myst_dmath_double_inline: true  # Allow display math ($$) within an inline context
 
 #######################################################################################
 # HTML-specific settings
@@ -72,6 +74,7 @@ repository:
 #######################################################################################
 # Advanced and power-user settings
 sphinx:
-  extra_extensions: # A list of extra extensions to load by Sphinx (added to those already used by JB).
-  config: # key-value pairs to directly over-ride the Sphinx configuration
+  extra_extensions          : # A list of extra extensions to load by Sphinx (added to those already used by JB).
+  local_extensions          :   # A list of local extensions to load by sphinx specified by "name: path" items
+  config                    : # key-value pairs to directly over-ride the Sphinx configuration
 

content/GLM_Single_Subject_Model.ipynb

@@ -1112,13 +1112,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "```{note}\n",
-    "Multicollineary as discussed above can lead to unstable estimates of the regression coefficients, particularly for the regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model. \n",
+    "As discussed above, multicollineary can lead to unstable estimates of the regression coefficients, which is particularly important to keep in mind for regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model. \n",
     "\n",
     "A simple fix to this problem is to use the `.clean()` method. This method will remove any columns that are perfectly collinear with other columns in the design matrix.\n",
     "```"