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diff --git a/_images/dte_empirical.png b/_images/dte_empirical.png
diff --git a/_images/dte_moment.png b/_images/dte_moment.png
diff --git a/_images/dte_simple.png b/_images/dte_simple.png
diff --git a/_images/dte_uniform.png b/_images/dte_uniform.png
diff --git a/_images/pte_simple.png b/_images/pte_simple.png
diff --git a/_images/qte.png b/_images/qte.png
diff --git a/_modules/dte_adj.html b/_modules/dte_adj.html
diff --git a/_modules/dte_adj/plot.html b/_modules/dte_adj/plot.html
@@ -123,6 +123,7 @@ <h3>Navigation</h3>
 <li class="toctree-l1"><a class="reference internal" href="../../installation.html">Installation Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../get_started.html">Get Started</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">Modules</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contribution Guide</a></li>
 </ul>
 
 <div class="relations">

diff --git a/_modules/index.html b/_modules/index.html
@@ -55,6 +55,7 @@ <h3>Navigation</h3>
 <li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../get_started.html">Get Started</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../modules.html">Modules</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributing.html">Contribution Guide</a></li>
 </ul>
 
 <div class="relations">

diff --git a/_sources/contributing.rst.txt b/_sources/contributing.rst.txt
@@ -0,0 +1,4 @@
+Contribution Guide
+==================
+
+Regarding how to contribute to this package, please refer to https://github.com/CyberAgentAILab/python-dte-adjustment/CONTRIBUTING.md for more details.
diff --git a/_sources/get_started.rst.txt b/_sources/get_started.rst.txt
@@ -43,11 +43,11 @@ Generate data for training cumulative distribution function:
       quadratic_term = np.dot(X**2, gamma)
       
       # Outcome equation
-      Y = D + linear_term + quadratic_term + U
+      Y = 5 * D + linear_term + quadratic_term + U
       
       return X, D, Y
 
-  n = 100  # Sample size
+  n = 1000  # Sample size
   X, D, Y = generate_data(n)
 
 Then, let's build an empirical cumulative distribution function (CDF).
@@ -63,13 +63,14 @@ Distributional treatment effect (DTE) can be computed easily in the following co
 
 .. code-block:: python
 
-  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=np.sort(Y), variance_type="simple")
+  locations = np.linspace(Y.min(), Y.max(), 20)
+  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=locations, variance_type="simple")
 
 A convenience function is available to visualize distribution effects. This method can be used for other distribution parameters including Probability Treatment Effect (PTE) and Quantile Treatment Effect (QTE).
 
 .. code-block:: python
 
-  plot(np.sort(Y), dte, lower_bound, upper_bound, title="DTE of simple estimator")
+  plot(locations, dte, lower_bound, upper_bound, title="DTE of simple estimator")
 
 .. image:: _static/dte_empirical.png
    :alt: DTE of empirical estimator
@@ -92,8 +93,8 @@ DTE can be computed and visualized in the following code.
 
 .. code-block:: python
 
-  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=np.sort(Y), variance_type="simple")
-  plot(np.sort(Y), dte, lower_bound, upper_bound, title="DTE of adjusted estimator with simple confidence band")
+  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=locations, variance_type="simple")
+  plot(locations, dte, lower_bound, upper_bound, title="DTE of adjusted estimator with simple confidence band")
 
 .. image:: _static/dte_simple.png
    :alt: DTE of adjusted estimator with simple confidence band
@@ -105,8 +106,8 @@ Confidence bands can be computed in different ways. In the following code, we us
 
 .. code-block:: python
 
-  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=np.sort(Y), variance_type="moment")
-  plot(np.sort(Y), dte, lower_bound, upper_bound, title="DTE of adjusted estimator with moment confidence band")
+  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=locations, variance_type="moment")
+  plot(locations, dte, lower_bound, upper_bound, title="DTE of adjusted estimator with moment confidence band")
 
 .. image:: _static/dte_moment.png
    :alt: DTE of adjusted estimator with moment confidence band
@@ -118,8 +119,8 @@ Also, an uniform confidence band is used when "uniform" is specified for the "va
 
 .. code-block:: python
 
-  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=np.sort(Y), variance_type="uniform")
-  plot(np.sort(Y), dte, lower_bound, upper_bound, title="DTE of adjusted estimator with uniform confidence band")
+  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, locations=locations, variance_type="uniform")
+  plot(locations, dte, lower_bound, upper_bound, title="DTE of adjusted estimator with uniform confidence band")
 
 .. image:: _static/dte_uniform.png
    :alt: DTE of adjusted estimator with uniform confidence band
@@ -131,7 +132,6 @@ To compute PTE, we can use "predict_pte" method.
 
 .. code-block:: python
 
-  locations = np.linspace(Y.min(), Y.max(), 20)
   pte, lower_bound, upper_bound = estimator.predict_pte(target_treatment_arm=1, control_treatment_arm=0, width=1, locations=locations, variance_type="simple")
   plot(locations, pte, lower_bound, upper_bound, chart_type="bar", title="PTE of adjusted estimator with simple confidence band")
 

diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
@@ -3,20 +3,27 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-dte_adj Documentation
+dte_adj
 ===================================
 
+This a Python package for building the regression adjusted distribution function estimator proposed in "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction".
+
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :caption: Contents:
 
    installation
    get_started
    modules
+   contributing
 
 Indices and tables
-==================
+~~~~~~~~~~~~~~~~~~
 
 * :ref:`genindex`
 * :ref:`modindex`
-* :ref:`search`
+* :ref:`search`
+
+License
+~~~~~~~
+MIT License
diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt
@@ -3,13 +3,25 @@ Installation Guide
 
 This package can be installed either through PyPI or source code.
 
+Requirement
+~~~~~~~~~~~
+
+You need to use Python version 3.6 or higher to use this package.
+
+
 Install from PyPI
+~~~~~~~~~~~~~~~~~
+
+For installing the package from PyPI, please use the following command.
 
 .. code-block:: bash
 
    pip install dte_adj
 
 Install from source code
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+For installing the package from the source code, please use the following commands.
 
 .. code-block:: bash
 

diff --git a/_static/dte_empirical.png b/_static/dte_empirical.png
diff --git a/_static/dte_moment.png b/_static/dte_moment.png
diff --git a/_static/dte_simple.png b/_static/dte_simple.png
diff --git a/_static/dte_uniform.png b/_static/dte_uniform.png
diff --git a/_static/pte_simple.png b/_static/pte_simple.png
diff --git a/_static/qte.png b/_static/qte.png
diff --git a/contributing.html b/contributing.html
@@ -0,0 +1,110 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="./">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Contribution Guide &#8212; dte_adj 0.1.0 documentation</title>
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d1102ebc" />
+    <link rel="stylesheet" type="text/css" href="_static/alabaster.css?v=12dfc556" />
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+    <script src="_static/doctools.js?v=9a2dae69"></script>
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+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="prev" title="Modules" href="modules.html" />
+
+  <link rel="stylesheet" href="_static/custom.css" type="text/css" />
+
+
+
+
+
+  </head><body>
+
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+
+
+          <div class="body" role="main">
+
+  <section id="contribution-guide">
+<h1>Contribution Guide<a class="headerlink" href="#contribution-guide" title="Link to this heading">¶</a></h1>
+<p>Regarding how to contribute to this package, please refer to <a class="reference external" href="https://github.com/CyberAgentAILab/python-dte-adjustment/CONTRIBUTING.md">https://github.com/CyberAgentAILab/python-dte-adjustment/CONTRIBUTING.md</a> for more details.</p>
+</section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<h1 class="logo"><a href="index.html">dte_adj</a></h1>
+
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+
+<h3>Navigation</h3>
+<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="get_started.html">Get Started</a></li>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Contribution Guide</a></li>
+</ul>
+
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+  <li><a href="index.html">Documentation overview</a><ul>
+      <li>Previous: <a href="modules.html" title="previous chapter">Modules</a></li>
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diff --git a/genindex.html b/genindex.html
@@ -163,6 +163,7 @@ <h3>Navigation</h3>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="get_started.html">Get Started</a></li>
 <li class="toctree-l1"><a class="reference internal" href="modules.html">Modules</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contribution Guide</a></li>
 </ul>
 
 <div class="relations">