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diff --git a/.../87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb b/.../87bd7a5fc5edee5f7e46545ac3ff5913/Water_Drilling_Incident_Classification_with_Facet.ipynb
@@ -29,7 +29,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.       \n",
     "\n",
     "***\n",
     "\n",
@@ -111,7 +111,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -231,7 +231,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {

diff --git a/...oads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb b/...oads/cf9a77d79c2a3565a488450ce979e06f/Scikit-learn_classifier_summaries_using_FACET.ipynb
@@ -27,7 +27,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -89,7 +89,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, sklearn, etc.)\n",
     "2. Required FACET classes (i.e., selection)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) when using FACET"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",

diff --git a/docs/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb b/docs/_downloads/f8edc62901c3352636cbd43e4d813713/Classification_with_Facet.ipynb
@@ -31,7 +31,7 @@
     "    \n",
     "- **Enhanced Machine Learning Workflow**  \n",
     "\n",
-    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
+    "    FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-X-Official/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features.    \n",
     "\n",
     "***\n",
     "\n",
@@ -108,7 +108,7 @@
     "\n",
     "1. Common packages (pandas, matplotlib, etc.)\n",
     "2. Required FACET classes (inspection, selection, validation, simulation, etc.)\n",
-    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-Gamma/pytools)) when using FACET"
+    "3. Other BCG GAMMA packages which simplify pipelining (sklearndf, see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)) and support visualization (pytools, see on [GitHub](https://github.com/BCG-X-Official/pytools)) when using FACET"
    ]
   },
   {
@@ -162,7 +162,7 @@
    "source": [
     "**sklearndf imports**\n",
     "\n",
-    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-Gamma/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
+    "Instead of using the \"regular\" scikit-learn package, we are going to use sklearndf (see on [GitHub](https://github.com/orgs/BCG-X-Official/sklearndf/)). sklearndf is an open source library designed to address a common issue with scikit-learn: the outputs of transformers are numpy arrays, even when the input is a data frame. However, to inspect a model it is essential to keep track of the feature names. sklearndf retains all the functionality available through scikit-learn plus the feature traceability and usability associated with Pandas data frames. Additionally, the names of all your favourite scikit-learn functions are the same except for `DF` on the end. For example, the standard scikit-learn import:\n",
     "\n",
     "`from sklearn.pipeline import Pipeline`\n",
     "\n",
@@ -194,7 +194,7 @@
    "source": [
     "**pytools imports**\n",
     "\n",
-    "pytools (see on [GitHub](https://github.com/BCG-Gamma/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
+    "pytools (see on [GitHub](https://github.com/BCG-X-Official/pytools)) is an open source library containing general machine learning and visualization utilities, some of which are useful for visualising the advanced model inspection capabilities of FACET."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-Gamma/pytools) package also support a `style='text'` flag."
+    "For convenience when working in a non-notebook environment, all of the `Drawer`'s provided by the [pytools](https://github.com/BCG-X-Official/pytools) package also support a `style='text'` flag."
    ]
   },
   {

diff --git a/docs/_generated/getting_started.html b/docs/_generated/getting_started.html
@@ -261,72 +261,10 @@
 
   <section id="getting-started-with">
 <span id="getting-started"></span><h1>Getting started with<a class="headerlink" href="#getting-started-with" title="Permalink to this headline">#</a></h1>
-<img alt="../_images/Gamma_Facet_Logo_RGB_LB.svg" src="../_images/Gamma_Facet_Logo_RGB_LB.svg" /><div class="admonition note">
-<p class="admonition-title">Note</p>
-<p><strong>FACET 2.0 now available!</strong></p>
-<p>FACET 2.0 brings numerous API enhancements and improvements, accelerates model
-inspection by up to a factor of 50 in many practical applications, introduces a new,
-more flexible and user-friendly API for hyperparameter tuning – with support for
-<cite>scikit-learn</cite>’s native hyperparameter searchers – and improves the styling of all
-visualizations.</p>
-<p>See the <a class="reference external" href="https://bcg-gamma.github.io/facet/docs-version/2-0/_generated/release_notes.html">release notes</a>
-for more details.</p>
-</div>
-<p>FACET is an open source library for human-explainable AI.
+<img alt="../_images/Gamma_Facet_Logo_RGB_LB.svg" src="../_images/Gamma_Facet_Logo_RGB_LB.svg" /><p>FACET is an open source library for human-explainable AI.
 It combines sophisticated model inspection and model-based simulation to enable better
 explanations of your supervised machine learning models.</p>
 <p>FACET is composed of the following key components:</p>
-<table class="table">
-<colgroup>
-<col style="width: 19%" />
-<col style="width: 81%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p>      </p>
-<p><a class="facet-icon reference internal" href="../_images/inspect_icon.png"><img alt="inspect" class="facet-icon" src="../_images/inspect_icon.png" style="width: 100px;" /></a></p>
-</td>
-<td><p><strong>Model Inspection</strong></p>
-<p>FACET introduces a new algorithm to quantify dependencies and
-interactions between features in ML models.
-This new tool for human-explainable AI adds a new, global
-perspective to the observation-level explanations provided by the
-popular <a class="reference external" href="https://shap.readthedocs.io/en/stable/">SHAP</a> approach.
-To learn more about FACET’s model inspection capabilities, see the
-getting started example below.</p>
-</td>
-</tr>
-<tr class="row-even"><td><p>      </p>
-<p><a class="facet-icon reference internal" href="../_images/sim_icon.png"><img alt="sim" class="facet-icon" src="../_images/sim_icon.png" style="width: 100px;" /></a></p>
-</td>
-<td><p><strong>Model Simulation</strong></p>
-<p>FACET’s model simulation algorithms use ML models for
-<em>virtual experiments</em> to help identify scenarios that optimise
-predicted outcomes.
-To quantify the uncertainty in simulations, FACET utilises a range
-of bootstrapping algorithms including stationary and stratified
-bootstraps.
-For an example of FACET’s bootstrap simulations, see the
-quickstart example below.</p>
-</td>
-</tr>
-<tr class="row-odd"><td><p>      </p>
-<p><a class="facet-icon reference internal" href="../_images/pipe_icon.png"><img alt="pipe" class="facet-icon" src="../_images/pipe_icon.png" style="width: 100px;" /></a></p>
-</td>
-<td><p><strong>Enhanced Machine Learning Workflow</strong></p>
-<p>FACET offers an efficient and transparent machine learning
-workflow, enhancing
-<a class="reference external" href="https://scikit-learn.org/stable/index.html">scikit-learn</a>’s
-tried and tested pipelining paradigm with new capabilities for model
-selection, inspection, and simulation.
-FACET also introduces
-<a class="reference external" href="https://github.com/BCG-Gamma/sklearndf">sklearndf</a>
-[<a class="reference external" href="https://bcg-gamma.github.io/sklearndf/index.html">documentation</a>]
-an augmented version of <em>scikit-learn</em> with enhanced support for
-<em>pandas</em> data frames that ensures end-to-end traceability of features.</p>
-</td>
-</tr>
-</tbody>
-</table>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">#</a></h2>
 <p>FACET supports both PyPI and Anaconda.
@@ -364,9 +302,9 @@ <h2>Quickstart<a class="headerlink" href="#quickstart" title="Permalink to this
 <p>The following quickstart guide provides a minimal example workflow to get you
 up and running with FACET.
 For additional tutorials and the API reference,
-see the <a class="reference external" href="https://bcg-gamma.github.io/facet/docs-version/2-0">FACET documentation</a>.</p>
+see the <a class="reference external" href="https://bcg-x-official.github.io/facet/docs-version/2-0">FACET documentation</a>.</p>
 <p>Changes and additions to new versions are summarized in the
-<a class="reference external" href="https://bcg-gamma.github.io/facet/docs-version/2-0/release_notes.html">release notes</a>.</p>
+<a class="reference external" href="https://bcg-x-official.github.io/facet/docs-version/2-0/release_notes.html">release notes</a>.</p>
 <section id="enhanced-machine-learning-workflow">
 <h3>Enhanced Machine Learning Workflow<a class="headerlink" href="#enhanced-machine-learning-workflow" title="Permalink to this headline">#</a></h3>
 <p>To demonstrate the model inspection capability of FACET, we first create a
@@ -572,7 +510,7 @@ <h3>Model Inspection<a class="headerlink" href="#model-inspection" title="Permal
 removing <cite>TCH</cite>, and one of <cite>TC</cite> or <cite>LDL</cite> to further simplify the model and obtain a
 reduced set of independent features.</p>
 <p>Please see the
-<a class="reference external" href="https://bcg-gamma.github.io/facet/apidoc/facet.html">API reference</a>
+<a class="reference external" href="https://bcg-x-official.github.io/facet/apidoc/facet.html">API reference</a>
 for more detail.</p>
 </section>
 <section id="model-simulation">
@@ -630,17 +568,17 @@ <h2>Contributing<a class="headerlink" href="#contributing" title="Permalink to t
 <p>FACET is stable and is being supported long-term.</p>
 <p>Contributions to FACET are welcome and appreciated.
 For any bug reports or feature requests/enhancements please use the appropriate
-<a class="reference external" href="https://github.com/BCG-Gamma/facet/issues">GitHub form</a>, and if you wish to do so,
+<a class="reference external" href="https://github.com/BCG-X-Official/facet/issues">GitHub form</a>, and if you wish to do so,
 please open a PR addressing the issue.</p>
 <p>We do ask that for any major changes please discuss these with us first via an issue or
 using our team email: <a class="reference external" href="mailto:FacetTeam&#37;&#52;&#48;bcg&#46;com">FacetTeam<span>&#64;</span>bcg<span>&#46;</span>com</a>.</p>
 <p>For further information on contributing please see our
-<a class="reference external" href="https://bcg-gamma.github.io/facet/contribution_guide.html">contribution guide</a>.</p>
+<a class="reference external" href="https://bcg-x-official.github.io/facet/contribution_guide.html">contribution guide</a>.</p>
 </section>
 <section id="license">
 <h2>License<a class="headerlink" href="#license" title="Permalink to this headline">#</a></h2>
 <p>FACET is licensed under Apache 2.0 as described in the
-<a class="reference external" href="https://github.com/BCG-Gamma/facet/blob/develop/LICENSE">LICENSE</a> file.</p>
+<a class="reference external" href="https://github.com/BCG-X-Official/facet/blob/develop/LICENSE">LICENSE</a> file.</p>
 </section>
 <section id="acknowledgements">
 <h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h2>
@@ -657,7 +595,7 @@ <h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permal
 <section id="bcg-gamma">
 <h2>BCG GAMMA<a class="headerlink" href="#bcg-gamma" title="Permalink to this headline">#</a></h2>
 <p>If you would like to know more about the team behind FACET please see the
-<a class="reference external" href="https://bcg-gamma.github.io/facet/about_us.html">about us</a> page.</p>
+<a class="reference external" href="https://bcg-x-official.github.io/facet/about_us.html">about us</a> page.</p>
 <p>We are always on the lookout for passionate and talented data scientists to join the
 BCG GAMMA team. If you would like to know more you can find out about
 <a class="reference external" href="https://www.bcg.com/en-gb/beyond-consulting/bcg-gamma/default">BCG GAMMA</a>,