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diff --git a/api/_modules/aepsych/factory/default.html b/api/_modules/aepsych/factory/default.html
@@ -53,6 +53,7 @@ <h1>Source code for aepsych.factory.default</h1><div class="highlight"><pre>
 <span class="sd">            config details).</span>
 <span class="sd">        dim (int, optional): Dimensionality of the parameter space. Must be provided</span>
 <span class="sd">            if config is None.</span>
+<span class="sd">        stimuli_per_trial (int): Number of stimuli per trial. Defaults to 1.</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        Tuple[gpytorch.means.Mean, gpytorch.kernels.Kernel]: Instantiated</span>
@@ -86,6 +87,14 @@ <h1>Source code for aepsych.factory.default</h1><div class="highlight"><pre>
 <span class="k">def</span> <span class="nf">_get_default_mean_function</span><span class="p">(</span>
     <span class="n">config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Config</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""Creates a default mean function for Gaussian Processes.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config, optional): Configuration object.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        gpytorch.means.ConstantMean: An instantiated mean function with appropriate priors based on the configuration.</span>
+<span class="sd">    """</span>
     <span class="c1"># default priors</span>
     <span class="n">fixed_mean</span> <span class="o">=</span> <span class="kc">False</span>
     <span class="n">mean</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">()</span>
@@ -111,6 +120,18 @@ <h1>Source code for aepsych.factory.default</h1><div class="highlight"><pre>
     <span class="n">stimuli_per_trial</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
     <span class="n">active_dims</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""Creates a default covariance function for Gaussian Processes.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config, optional): Configuration object.</span>
+<span class="sd">        dim (int): Dimensionality of the parameter space.</span>
+<span class="sd">        stimuli_per_trial (int): Number of stimuli per trial.</span>
+<span class="sd">        active_dims (List[int], optional): List of dimensions to use in the covariance function. Defaults to None.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        gpytorch.kernels.Kernel: An instantiated kernel with appropriate priors based on the configuration.</span>
+<span class="sd">    """</span>
+
     <span class="c1"># default priors</span>
     <span class="n">lengthscale_prior</span> <span class="o">=</span> <span class="s2">"lognormal"</span> <span class="k">if</span> <span class="n">stimuli_per_trial</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">"gamma"</span>
     <span class="n">ls_loc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>

diff --git a/api/_modules/aepsych/factory/default/index.html b/api/_modules/aepsych/factory/default/index.html
@@ -53,6 +53,7 @@ <h1>Source code for aepsych.factory.default</h1><div class="highlight"><pre>
 <span class="sd">            config details).</span>
 <span class="sd">        dim (int, optional): Dimensionality of the parameter space. Must be provided</span>
 <span class="sd">            if config is None.</span>
+<span class="sd">        stimuli_per_trial (int): Number of stimuli per trial. Defaults to 1.</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        Tuple[gpytorch.means.Mean, gpytorch.kernels.Kernel]: Instantiated</span>
@@ -86,6 +87,14 @@ <h1>Source code for aepsych.factory.default</h1><div class="highlight"><pre>
 <span class="k">def</span> <span class="nf">_get_default_mean_function</span><span class="p">(</span>
     <span class="n">config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Config</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""Creates a default mean function for Gaussian Processes.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config, optional): Configuration object.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        gpytorch.means.ConstantMean: An instantiated mean function with appropriate priors based on the configuration.</span>
+<span class="sd">    """</span>
     <span class="c1"># default priors</span>
     <span class="n">fixed_mean</span> <span class="o">=</span> <span class="kc">False</span>
     <span class="n">mean</span> <span class="o">=</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">()</span>
@@ -111,6 +120,18 @@ <h1>Source code for aepsych.factory.default</h1><div class="highlight"><pre>
     <span class="n">stimuli_per_trial</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
     <span class="n">active_dims</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">Kernel</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""Creates a default covariance function for Gaussian Processes.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config, optional): Configuration object.</span>
+<span class="sd">        dim (int): Dimensionality of the parameter space.</span>
+<span class="sd">        stimuli_per_trial (int): Number of stimuli per trial.</span>
+<span class="sd">        active_dims (List[int], optional): List of dimensions to use in the covariance function. Defaults to None.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        gpytorch.kernels.Kernel: An instantiated kernel with appropriate priors based on the configuration.</span>
+<span class="sd">    """</span>
+
     <span class="c1"># default priors</span>
     <span class="n">lengthscale_prior</span> <span class="o">=</span> <span class="s2">"lognormal"</span> <span class="k">if</span> <span class="n">stimuli_per_trial</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">"gamma"</span>
     <span class="n">ls_loc</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>

diff --git a/api/_modules/aepsych/factory/ordinal.html b/api/_modules/aepsych/factory/ordinal.html
@@ -35,6 +35,16 @@ <h1>Source code for aepsych.factory.ordinal</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="ordinal_mean_covar_factory"><a class="viewcode-back" href="../../../factory.html#aepsych.factory.ordinal_mean_covar_factory">[docs]</a><span class="k">def</span> <span class="nf">ordinal_mean_covar_factory</span><span class="p">(</span>
     <span class="n">config</span><span class="p">:</span> <span class="n">Config</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">,</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">ScaleKernel</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">""" Create a mean and covariance function for ordinal GPs.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config): Config object containing bounds.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple[gpytorch.means.ConstantMean, gpytorch.kernels.ScaleKernel]: A tuple containing</span>
+<span class="sd">        the mean function (ConstantMean) and the covariance function (ScaleKernel).</span>
+<span class="sd">    """</span>
+
     <span class="k">try</span><span class="p">:</span>
         <span class="n">base_factory</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">getobj</span><span class="p">(</span><span class="s2">"ordinal_mean_covar_factory"</span><span class="p">,</span> <span class="s2">"base_factory"</span><span class="p">)</span>
     <span class="k">except</span> <span class="n">NoOptionError</span><span class="p">:</span>

diff --git a/api/_modules/aepsych/factory/ordinal/index.html b/api/_modules/aepsych/factory/ordinal/index.html
@@ -35,6 +35,16 @@ <h1>Source code for aepsych.factory.ordinal</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="ordinal_mean_covar_factory"><a class="viewcode-back" href="../../../factory.html#aepsych.factory.ordinal_mean_covar_factory">[docs]</a><span class="k">def</span> <span class="nf">ordinal_mean_covar_factory</span><span class="p">(</span>
     <span class="n">config</span><span class="p">:</span> <span class="n">Config</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">,</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">ScaleKernel</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">""" Create a mean and covariance function for ordinal GPs.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config): Config object containing bounds.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple[gpytorch.means.ConstantMean, gpytorch.kernels.ScaleKernel]: A tuple containing</span>
+<span class="sd">        the mean function (ConstantMean) and the covariance function (ScaleKernel).</span>
+<span class="sd">    """</span>
+
     <span class="k">try</span><span class="p">:</span>
         <span class="n">base_factory</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">getobj</span><span class="p">(</span><span class="s2">"ordinal_mean_covar_factory"</span><span class="p">,</span> <span class="s2">"base_factory"</span><span class="p">)</span>
     <span class="k">except</span> <span class="n">NoOptionError</span><span class="p">:</span>

diff --git a/api/_modules/aepsych/factory/pairwise.html b/api/_modules/aepsych/factory/pairwise.html
@@ -33,6 +33,14 @@ <h1>Source code for aepsych.factory.pairwise</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="pairwise_mean_covar_factory"><a class="viewcode-back" href="../../../factory.html#aepsych.factory.pairwise_mean_covar_factory">[docs]</a><span class="k">def</span> <span class="nf">pairwise_mean_covar_factory</span><span class="p">(</span>
     <span class="n">config</span><span class="p">:</span> <span class="n">Config</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">,</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">ScaleKernel</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">""" Creates a mean and covariance function for pairwise GPs.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config): Config object containing bounds.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple[gpytorch.means.ConstantMean, gpytorch.kernels.ScaleKernel]: A tuple containing</span>
+<span class="sd">        the mean function (ConstantMean) and the covariance function (ScaleKernel)."""</span>
     <span class="n">lb</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">gettensor</span><span class="p">(</span><span class="s2">"common"</span><span class="p">,</span> <span class="s2">"lb"</span><span class="p">)</span>
     <span class="n">ub</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">gettensor</span><span class="p">(</span><span class="s2">"common"</span><span class="p">,</span> <span class="s2">"ub"</span><span class="p">)</span>
     <span class="k">assert</span> <span class="n">lb</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">ub</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"bounds shape mismatch!"</span>

diff --git a/api/_modules/aepsych/factory/pairwise/index.html b/api/_modules/aepsych/factory/pairwise/index.html
@@ -33,6 +33,14 @@ <h1>Source code for aepsych.factory.pairwise</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="pairwise_mean_covar_factory"><a class="viewcode-back" href="../../../factory.html#aepsych.factory.pairwise_mean_covar_factory">[docs]</a><span class="k">def</span> <span class="nf">pairwise_mean_covar_factory</span><span class="p">(</span>
     <span class="n">config</span><span class="p">:</span> <span class="n">Config</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">gpytorch</span><span class="o">.</span><span class="n">means</span><span class="o">.</span><span class="n">ConstantMean</span><span class="p">,</span> <span class="n">gpytorch</span><span class="o">.</span><span class="n">kernels</span><span class="o">.</span><span class="n">ScaleKernel</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">""" Creates a mean and covariance function for pairwise GPs.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        config (Config): Config object containing bounds.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        Tuple[gpytorch.means.ConstantMean, gpytorch.kernels.ScaleKernel]: A tuple containing</span>
+<span class="sd">        the mean function (ConstantMean) and the covariance function (ScaleKernel)."""</span>
     <span class="n">lb</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">gettensor</span><span class="p">(</span><span class="s2">"common"</span><span class="p">,</span> <span class="s2">"lb"</span><span class="p">)</span>
     <span class="n">ub</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">gettensor</span><span class="p">(</span><span class="s2">"common"</span><span class="p">,</span> <span class="s2">"ub"</span><span class="p">)</span>
     <span class="k">assert</span> <span class="n">lb</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">ub</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s2">"bounds shape mismatch!"</span>