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diff --git a/docs/datasets.html b/docs/datasets.html
@@ -20,15 +20,6 @@
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@@ -127,4 +118,4 @@ <h1>Index</h1>
 <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.9.1</a>.</p>
 </footer>
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-</html>
+</html>
diff --git a/docs/dsm_api.html b/docs/dsm_api.html
@@ -218,6 +218,30 @@ <h2 id="returns">Returns</h2>
 <dd>numpy array of the survival probabilites at each time in t.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepSurvivalMachines.predict_pdf"><code class="name flex">
+<span>def <span class="ident">predict_pdf</span></span>(<span>self, x, t, risk=1)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DSMBase" href="#dsm.dsm_api.DSMBase">DSMBase</a></code>.<code><a title="dsm.dsm_api.DSMBase.predict_pdf" href="#dsm.dsm_api.DSMBase.predict_pdf">predict_pdf</a></code>
+</p>
+<div class="desc"><p>Returns the estimated pdf at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
+<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T = t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T = t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>. </p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>list</code> or <code>float</code></dt>
+<dd>a list or float of the times at which pdf is
+to be computed</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>np.array</code></dt>
+<dd>numpy array of the estimated pdf at each time in t.</dd>
+</dl></div>
+</dd>
 </dl>
 </dd>
 <dt id="dsm.dsm_api.DeepRecurrentSurvivalMachines"><code class="flex name class">
@@ -226,7 +250,12 @@ <h2 id="returns">Returns</h2>
 </code></dt>
 <dd>
 <div class="desc"><p>The Deep Recurrent Survival Machines model to handle data with
-time-dependent covariates.</p></div>
+time-dependent covariates.</p>
+<p>For full details on Deep Recurrent Survival Machines, refer to our paper [1].</p>
+<h2 id="references">References</h2>
+<p>[1] <a href="http://proceedings.mlr.press/v146/nagpal21a.html">
+Deep Parametric Time-to-Event Regression with Time-Varying Covariates
+AAAI Spring Symposium on Survival Prediction</a></p></div>
 <div class="git-link-div"><a href="https://github.com/autonlab/DeepSurvivalMachines" class="git-link">Browse git</a></div>
 <h3>Methods</h3>
 <dl>
@@ -364,6 +393,30 @@ <h2 id="returns">Returns</h2>
 <dd>numpy array of the survival probabilites at each time in t.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_pdf"><code class="name flex">
+<span>def <span class="ident">predict_pdf</span></span>(<span>self, x, t, risk=1)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DSMBase" href="#dsm.dsm_api.DSMBase">DSMBase</a></code>.<code><a title="dsm.dsm_api.DSMBase.predict_pdf" href="#dsm.dsm_api.DSMBase.predict_pdf">predict_pdf</a></code>
+</p>
+<div class="desc"><p>Returns the estimated pdf at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
+<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T = t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T = t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>. </p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>list</code> or <code>float</code></dt>
+<dd>a list or float of the times at which pdf is
+to be computed</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>np.array</code></dt>
+<dd>numpy array of the estimated pdf at each time in t.</dd>
+</dl></div>
+</dd>
 </dl>
 </dd>
 <dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines"><code class="flex name class">
@@ -510,6 +563,30 @@ <h2 id="returns">Returns</h2>
 <dd>numpy array of the survival probabilites at each time in t.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_pdf"><code class="name flex">
+<span>def <span class="ident">predict_pdf</span></span>(<span>self, x, t, risk=1)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DSMBase" href="#dsm.dsm_api.DSMBase">DSMBase</a></code>.<code><a title="dsm.dsm_api.DSMBase.predict_pdf" href="#dsm.dsm_api.DSMBase.predict_pdf">predict_pdf</a></code>
+</p>
+<div class="desc"><p>Returns the estimated pdf at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
+<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T = t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T = t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>. </p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>list</code> or <code>float</code></dt>
+<dd>a list or float of the times at which pdf is
+to be computed</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>np.array</code></dt>
+<dd>numpy array of the estimated pdf at each time in t.</dd>
+</dl></div>
+</dd>
 </dl>
 </dd>
 <dt id="dsm.dsm_api.DeepCNNRNNSurvivalMachines"><code class="flex name class">
@@ -656,6 +733,30 @@ <h2 id="returns">Returns</h2>
 <dd>numpy array of the survival probabilites at each time in t.</dd>
 </dl></div>
 </dd>
+<dt id="dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_pdf"><code class="name flex">
+<span>def <span class="ident">predict_pdf</span></span>(<span>self, x, t, risk=1)</span>
+</code></dt>
+<dd>
+<p class="inheritance">
+<em>Inherited from:</em>
+<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></code>.<code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_pdf" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_pdf">predict_pdf</a></code>
+</p>
+<div class="desc"><p>Returns the estimated pdf at time <span><span class="MathJax_Preview"> t </span><script type="math/tex"> t </script></span>,
+<span><span class="MathJax_Preview"> \widehat{\mathbb{P}}(T = t|X) </span><script type="math/tex"> \widehat{\mathbb{P}}(T = t|X) </script></span> for some input data <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>. </p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>np.ndarray</code></dt>
+<dd>A numpy array of the input features, <span><span class="MathJax_Preview"> x </span><script type="math/tex"> x </script></span>.</dd>
+<dt><strong><code>t</code></strong> :&ensp;<code>list</code> or <code>float</code></dt>
+<dd>a list or float of the times at which pdf is
+to be computed</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>np.array</code></dt>
+<dd>numpy array of the estimated pdf at each time in t.</dd>
+</dl></div>
+</dd>
 </dl>
 </dd>
 </dl>
@@ -677,42 +778,46 @@ <h1>Index</h1>
 <ul>
 <li>
 <h4><code><a title="dsm.dsm_api.DeepSurvivalMachines" href="#dsm.dsm_api.DeepSurvivalMachines">DeepSurvivalMachines</a></code></h4>
-<ul class="">
+<ul class="two-column">
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.fit" href="#dsm.dsm_api.DeepSurvivalMachines.fit">fit</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepSurvivalMachines.predict_survival">predict_survival</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepSurvivalMachines.predict_pdf" href="#dsm.dsm_api.DeepSurvivalMachines.predict_pdf">predict_pdf</a></code></li>
 </ul>
 </li>
 <li>
 <h4><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines">DeepRecurrentSurvivalMachines</a></code></h4>
-<ul class="">
+<ul class="two-column">
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.fit" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.fit">fit</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_survival">predict_survival</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_pdf" href="#dsm.dsm_api.DeepRecurrentSurvivalMachines.predict_pdf">predict_pdf</a></code></li>
 </ul>
 </li>
 <li>
 <h4><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines">DeepConvolutionalSurvivalMachines</a></code></h4>
-<ul class="">
+<ul class="two-column">
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.fit" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.fit">fit</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_survival">predict_survival</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_pdf" href="#dsm.dsm_api.DeepConvolutionalSurvivalMachines.predict_pdf">predict_pdf</a></code></li>
 </ul>
 </li>
 <li>
 <h4><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines">DeepCNNRNNSurvivalMachines</a></code></h4>
-<ul class="">
+<ul class="two-column">
 <li><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines.fit" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines.fit">fit</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines.compute_nll" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines.compute_nll">compute_nll</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_mean" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_mean">predict_mean</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_risk" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_risk">predict_risk</a></code></li>
 <li><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_survival" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_survival">predict_survival</a></code></li>
+<li><code><a title="dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_pdf" href="#dsm.dsm_api.DeepCNNRNNSurvivalMachines.predict_pdf">predict_pdf</a></code></li>
 </ul>
 </li>
 </ul>

diff --git a/docs/dsm_torch.html b/docs/dsm_torch.html
@@ -236,7 +236,7 @@ <h2 id="args">Args</h2>
 </dd>
 <dt id="dsm.dsm_torch.DeepConvolutionalSurvivalMachinesTorch"><code class="flex name class">
 <span>class <span class="ident">DeepConvolutionalSurvivalMachinesTorch</span></span>
-<span>(</span><span>inputdim, k, typ='ConvNet', hidden=None, dist='Weibull', temp=1000.0, discount=1.0, optimizer='Adam', risks=1)</span>
+<span>(</span><span>inputdim, k, embedding=None, hidden=None, dist='Weibull', temp=1000.0, discount=1.0, optimizer='Adam', risks=1)</span>
 </code></dt>
 <dd>
 <div class="desc"><p>A Torch implementation of Deep Convolutional Survival Machines model.</p>
@@ -258,11 +258,10 @@ <h2 id="parameters">Parameters</h2>
 <dd>Dimensionality of the input features. A tuple (height, width).</dd>
 <dt><strong><code>k</code></strong> :&ensp;<code>int</code></dt>
 <dd>The number of underlying parametric distributions.</dd>
+<dt><strong><code>embedding</code></strong> :&ensp;<code>torch.nn.Module</code></dt>
+<dd>A torch CNN to obtain the representation of the input data.</dd>
 <dt><strong><code>hidden</code></strong> :&ensp;<code>int</code></dt>
 <dd>The number of neurons in each hidden layer.</dd>
-<dt><strong><code>init</code></strong> :&ensp;<code>tuple</code></dt>
-<dd>A tuple for initialization of the parameters for the underlying
-distributions. (shape, scale).</dd>
 <dt><strong><code>dist</code></strong> :&ensp;<code>str</code></dt>
 <dd>Choice of the underlying survival distributions.
 One of 'Weibull', 'LogNormal'.

diff --git a/docs/index.html b/docs/index.html
@@ -100,37 +100,18 @@ <h2 id="example-usage">Example Usage</h2>
 &gt;&gt;&gt; # estimate the predicted risks at the time
 &gt;&gt;&gt; model.predict_risk(x, 10)
 </code></pre>
-<h2 id="installation">Installation</h2>
-<pre><code class="language-console">foo@bar:~$ git clone https://github.com/autonlab/DeepSurvivalMachines.git
-foo@bar:~$ cd DeepSurvivalMachines
-foo@bar:~$ pip install -r requirements.txt
-</code></pre>
-<h2 id="examples">Examples</h2>
-<ol>
-<li><a href="https://nbviewer.jupyter.org/github/autonlab/DeepSurvivalMachines/blob/master/examples/DSM%20on%20SUPPORT%20Dataset.ipynb">Deep Survival Machines on the SUPPORT Dataset</a></li>
-</ol>
 <h2 id="references">References</h2>
 <p>Please cite the following papers if you are using the <code><a title="dsm" href="#dsm">dsm</a></code> package.</p>
 <p>[1] <a href="https://arxiv.org/abs/2003.01176">Deep Survival Machines:
 Fully Parametric Survival Regression and
-Representation Learning for Censored Data with Competing Risks.
-IEEE Journal of Biomedical \&amp; Health Informatics (2021)</a></a></p>
+Representation Learning for Censored Data with Competing Risks."
+arXiv preprint arXiv:2003.01176 (2020)</a></a></p>
 <pre><code>  @article{nagpal2020deep,
   title={Deep Survival Machines: Fully Parametric Survival Regression and\
   Representation Learning for Censored Data with Competing Risks},
   author={Nagpal, Chirag and Li, Xinyu and Dubrawski, Artur},
-  journal={IEEE Journal of Biomedical and Health Informatics},
-  year={2021}
-  }
-</code></pre>
-<p>[2] <a href="">Recurrent Deep Survival Machines:
-Deep Parametric Time-to-Event Regression with Time-Varying Covariates.
-AAAI Spring Symposium (2021)</a></a></p>
-<pre><code>  @article{nagpal2021rdsm,
-  title={Deep Parametric Time-to-Event Regression with Time-Varying Covariates},
-  author={Nagpal, Chirag and Jeanselme, Vincent and Dubrawski, Artur},
-  journal={AAAI Spring Symposium on Survival Analysis},
-  year={2021}
+  journal={arXiv preprint arXiv:2003.01176},
+  year={2020}
   }
 </code></pre>
 <h2 id="compatibility">Compatibility</h2>
@@ -168,6 +149,30 @@ <h2 id="license">License</h2>
 <section>
 <h2 class="section-title" id="header-submodules">Sub-modules</h2>
 <dl>
+<dt><code class="name"><a title="dsm.contrib" href="contrib/index.html">dsm.contrib</a></code></dt>
+<dd>
+<div class="desc"><p><code><a title="dsm" href="#dsm">dsm</a></code> includes extended functionality for survival analysis as part
+of <code><a title="dsm.contrib" href="contrib/index.html">dsm.contrib</a></code>.</p>
+<h2 id="contributed-modules">Contributed Modules</h2>
+<p>This submodule incorporates contributed survival analysis methods.</p>
+<h2 id="deep-cox-mixtures">Deep Cox Mixtures</h2>
+<p>The Cox Mixture involves the assumption that the survival function
+of the individual to be a mixture of K Cox Models. Conditioned on each
+subgroup Z=k; the PH assumptions are assumed to hold and the baseline
+hazard rates is determined non-parametrically using an spline-interpolated
+Breslow's estimator.</p>
+<p>For full details on Deep Cox Mixture, refer to the paper [1].</p>
+<h2 id="references">References</h2>
+<p>[1] <a href="https://arxiv.org/abs/2101.06536">Deep Cox Mixtures
+for Survival Regression. Machine Learning in Health Conference (2021)</a></p>
+<pre><code>  @article{nagpal2021dcm,
+  title={Deep Cox mixtures for survival regression},
+  author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
+  journal={arXiv preprint arXiv:2101.06536},
+  year={2021}
+  }
+</code></pre></div>
+</dd>
 <dt><code class="name"><a title="dsm.datasets" href="datasets.html">dsm.datasets</a></code></dt>
 <dd>
 <div class="desc"><p>Utility functions to load standard datasets to train and evaluate the
@@ -224,8 +229,6 @@ <h1>Index</h1>
 <li><a href="#deep-recurrent-survival-machines">Deep Recurrent Survival Machines</a></li>
 <li><a href="#deep-convolutional-survival-machines">Deep Convolutional Survival Machines</a></li>
 <li><a href="#example-usage">Example Usage</a></li>
-<li><a href="#installation">Installation</a></li>
-<li><a href="#examples">Examples</a></li>
 <li><a href="#references">References</a></li>
 <li><a href="#compatibility">Compatibility</a></li>
 <li><a href="#contributing">Contributing</a></li>
@@ -235,6 +238,7 @@ <h1>Index</h1>
 <ul id="index">
 <li><h3><a href="#header-submodules">Sub-modules</a></h3>
 <ul>
+<li><code><a title="dsm.contrib" href="contrib/index.html">dsm.contrib</a></code></li>
 <li><code><a title="dsm.datasets" href="datasets.html">dsm.datasets</a></code></li>
 <li><code><a title="dsm.dsm_api" href="dsm_api.html">dsm.dsm_api</a></code></li>
 <li><code><a title="dsm.dsm_torch" href="dsm_torch.html">dsm.dsm_torch</a></code></li>