Updated docs with working image links

AnotherSamWilson · Jul 28, 2024 · 95c4427 · 95c4427
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diff --git a/README.md b/README.md
@@ -932,7 +932,7 @@ each iteration, each specified variable in the dataset is imputed using
 the other variables in the dataset. These iterations should be run until
 it appears that convergence has been met.
 
-<img src="https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/MICEalgorithm.png" style="display: block; margin: auto;" />
+<img src="https://i.imgur.com/2L403kU.png" style="display: block; margin: auto;" />
 
 This process is continued until all specified variables have been
 imputed. Additional iterations can be run if it appears that the average
@@ -984,7 +984,7 @@ are selected, from which a value is chosen at random. This can be
 specified on a column-by-column basis. Going into more detail from our
 example above, we see how this works in practice:
 
-<img src="https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/PMM.png" style="display: block; margin: auto;" />
+<img src="https://i.imgur.com/3DBCXnL.png" style="display: block; margin: auto;" />
 
 This method is very useful if you have a variable which needs imputing
 which has any of the following characteristics:

diff --git a/README_gen.ipynb b/README_gen.ipynb
@@ -1281,7 +1281,7 @@
     "the other variables in the dataset. These iterations should be run until\n",
     "it appears that convergence has been met.\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/MICEalgorithm.png\" style=\"display: block; margin: auto;\" />\n",
+    "<img src=\"https://i.imgur.com/2L403kU.png\" style=\"display: block; margin: auto;\" />\n",
     "\n",
     "This process is continued until all specified variables have been\n",
     "imputed. Additional iterations can be run if it appears that the average\n",
@@ -1337,7 +1337,7 @@
     "specified on a column-by-column basis. Going into more detail from our\n",
     "example above, we see how this works in practice:\n",
     "\n",
-    "<img src=\"https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/PMM.png\" style=\"display: block; margin: auto;\" />\n",
+    "<img src=\"https://i.imgur.com/3DBCXnL.png\" style=\"display: block; margin: auto;\" />\n",
     "\n",
     "This method is very useful if you have a variable which needs imputing\n",
     "which has any of the following characteristics:\n",

diff --git a/docs/index.rst b/docs/index.rst
@@ -10,9 +10,36 @@ This documentation is meant to describe class methods and parameters only,
 for a thorough walkthrough of usage, please see the 
 `Github README <https://github.com/AnotherSamWilson/miceforest>`_.
 
+In general, the user will only be interacting with these two classes:
 
-Fast, memory efficient Multiple Imputation by Chained Equations (MICE)
-with lightgbm. The R version of this package may be found
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Classes:
+
+   ImputationKernel <ImputationKernel>
+   ImputedData <ImputedData>
+
+
+How miceforest Works
+--------------------
+
+Multiple Imputation by Chained Equations ‘fills in’ (imputes) missing
+data in a dataset through an iterative series of predictive models. In
+each iteration, each specified variable in the dataset is imputed using
+the other variables in the dataset. These iterations should be run until
+it appears that convergence has been met.
+
+.. image:: https://i.imgur.com/2L403kU.png
+   :target: https://github.com/AnotherSamWilson/miceforest?tab=readme-ov-file#the-mice-algorithm
+
+This process is continued until all specified variables have been
+imputed. Additional iterations can be run if it appears that the average
+imputed values have not converged, although no more than 5 iterations
+are usually necessary.
+
+This package provides fast, memory efficient Multiple Imputation by Chained 
+Equations (MICE) with lightgbm. The R version of this package may be found
 `here <https://github.com/FarrellDay/miceRanger>`_.
 
 `miceforest` was designed to be:
@@ -30,13 +57,4 @@ with lightgbm. The R version of this package may be found
       - Can impute new, unseen datasets quickly
       - Kernels are efficiently compressed during saving and loading
       - Data can be imputed in place to save memory
-      - Can build models on non-missing data
-
-
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Contents:
-
-   ImputationKernel <ImputationKernel>
-   ImputedData <ImputedData>
+      - Can build models on non-missing data