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diff --git a/_modules/arkouda/random/_generator.html b/_modules/arkouda/random/_generator.html
@@ -566,6 +566,58 @@ <h1>Source code for arkouda.random._generator</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">create_pdarray</span><span class="p">(</span><span class="n">rep_msg</span><span class="p">)</span></div>
 
 
+<div class="viewcode-block" id="Generator.normal">
+<a class="viewcode-back" href="../../../autoapi/arkouda/random/index.html#arkouda.Generator.normal">[docs]</a>
+    <span class="k">def</span> <span class="nf">normal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Draw samples from a normal (Gaussian) distribution</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        loc: float or pdarray of floats, optional</span>
+<span class="sd">            Mean of the distribution. Default of 0.</span>
+
+<span class="sd">        scale: float or pdarray of floats, optional</span>
+<span class="sd">            Standard deviation of the distribution. Must be non-negative. Default of 1.</span>
+
+<span class="sd">        size: numeric_scalars, optional</span>
+<span class="sd">            Output shape. Default is None, in which case a single value is returned.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The probability density for the Gaussian distribution is:</span>
+
+<span class="sd">        .. math::</span>
+<span class="sd">           p(x) = \frac{1}{\sqrt{2\pi \sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}</span>
+
+<span class="sd">        where :math:`\mu` is the mean and :math:`\sigma` the standard deviation.</span>
+<span class="sd">        The square of the standard deviation, :math:`\sigma^2`, is called the variance.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        pdarray</span>
+<span class="sd">            Pdarray of floats (unless size=None, in which case a single float is returned).</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        standard_normal</span>
+<span class="sd">        uniform</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ak.random.default_rng(17).normal(3, 2.5, 10)</span>
+<span class="sd">        array([2.3673425816523577 4.0532529435624589 2.0598322696795694])</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># delegate to numpy when return size is 1</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_np_generator</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span><span class="n">scale</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scale</span><span class="p">,</span> <span class="n">pdarray</span><span class="p">)</span> <span class="k">else</span> <span class="n">scale</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;scale must be non-negative.&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">loc</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span></div>
+
+
 <div class="viewcode-block" id="Generator.random">
 <a class="viewcode-back" href="../../../autoapi/arkouda/random/index.html#arkouda.Generator.random">[docs]</a>
     <span class="k">def</span> <span class="nf">random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -623,7 +675,7 @@ <h1>Source code for arkouda.random._generator</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="Generator.standard_normal">
 <a class="viewcode-back" href="../../../autoapi/arkouda/random/index.html#arkouda.Generator.standard_normal">[docs]</a>
     <span class="k">def</span> <span class="nf">standard_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Draw samples from a standard Normal distribution (mean=0, stdev=1).</span>
 
 <span class="sd">        Parameters</span>
@@ -638,10 +690,14 @@ <h1>Source code for arkouda.random._generator</h1><div class="highlight"><pre>
 
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
-<span class="sd">        For random samples from :math:`N(\\mu, \\sigma^2)`, use:</span>
+<span class="sd">        For random samples from :math:`N(\mu, \sigma^2)`, either call `normal` or do:</span>
 
-<span class="sd">        ``(sigma * standard_normal(size)) + mu``</span>
+<span class="sd">        .. math::</span>
+<span class="sd">            (\sigma \cdot \texttt{standard_normal}(size)) + \mu</span>
 
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        normal</span>
 
 <span class="sd">        Examples</span>
 <span class="sd">        --------</span>
@@ -651,12 +707,20 @@ <h1>Source code for arkouda.random._generator</h1><div class="highlight"><pre>
 <span class="sd">        &gt;&gt;&gt; rng.standard_normal(3)</span>
 <span class="sd">        array([0.8797352989638163, -0.7085325853376141, 0.021728052940979934])  # random</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="kn">from</span> <span class="nn">arkouda.random._legacy</span> <span class="kn">import</span> <span class="n">standard_normal</span>
-
         <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># delegate to numpy when return size is 1</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_np_generator</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">standard_normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_seed</span><span class="p">)</span></div>
+        <span class="n">rep_msg</span> <span class="o">=</span> <span class="n">generic_msg</span><span class="p">(</span>
+            <span class="n">cmd</span><span class="o">=</span><span class="s2">&quot;standardNormalGenerator&quot;</span><span class="p">,</span>
+            <span class="n">args</span><span class="o">=</span><span class="p">{</span>
+                <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name_dict</span><span class="p">[</span><span class="n">akfloat64</span><span class="p">],</span>
+                <span class="s2">&quot;size&quot;</span><span class="p">:</span> <span class="n">size</span><span class="p">,</span>
+                <span class="s2">&quot;state&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_state</span><span class="p">,</span>
+            <span class="p">},</span>
+        <span class="p">)</span>
+        <span class="c1"># since we generate 2*size uniform samples for box-muller transform</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_state</span> <span class="o">+=</span> <span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">create_pdarray</span><span class="p">(</span><span class="n">rep_msg</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="Generator.shuffle">

diff --git a/_sources/autoapi/arkouda/index.rst.txt b/_sources/autoapi/arkouda/index.rst.txt
@@ -10068,6 +10068,39 @@ Package Contents
 
 
 
+   .. py:method:: normal(loc=0.0, scale=1.0, size=None)
+
+      Draw samples from a normal (Gaussian) distribution
+
+      :param loc: Mean of the distribution. Default of 0.
+      :type loc: float or pdarray of floats, optional
+      :param scale: Standard deviation of the distribution. Must be non-negative. Default of 1.
+      :type scale: float or pdarray of floats, optional
+      :param size: Output shape. Default is None, in which case a single value is returned.
+      :type size: numeric_scalars, optional
+
+      .. rubric:: Notes
+
+      The probability density for the Gaussian distribution is:
+
+      .. math::
+         p(x) = \frac{1}{\sqrt{2\pi \sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}
+
+      where :math:`\mu` is the mean and :math:`\sigma` the standard deviation.
+      The square of the standard deviation, :math:`\sigma^2`, is called the variance.
+
+      :returns: Pdarray of floats (unless size=None, in which case a single float is returned).
+      :rtype: pdarray
+
+      .. seealso:: :obj:`standard_normal`, :obj:`uniform`
+
+      .. rubric:: Examples
+
+      >>> ak.random.default_rng(17).normal(3, 2.5, 10)
+      array([2.3673425816523577 4.0532529435624589 2.0598322696795694])
+
+
+
    .. py:method:: permutation(x)
 
       Randomly permute a sequence, or return a permuted range.
@@ -10134,9 +10167,12 @@ Package Contents
 
       .. rubric:: Notes
 
-      For random samples from :math:`N(\mu, \sigma^2)`, use:
+      For random samples from :math:`N(\mu, \sigma^2)`, either call `normal` or do:
+
+      .. math::
+          (\sigma \cdot \texttt{standard_normal}(size)) + \mu
 
-      ``(sigma * standard_normal(size)) + mu``
+      .. seealso:: :obj:`normal`
 
       .. rubric:: Examples
 

diff --git a/_sources/autoapi/arkouda/random/index.rst.txt b/_sources/autoapi/arkouda/random/index.rst.txt
@@ -107,6 +107,39 @@ Package Contents
 
 
 
+   .. py:method:: normal(loc=0.0, scale=1.0, size=None)
+
+      Draw samples from a normal (Gaussian) distribution
+
+      :param loc: Mean of the distribution. Default of 0.
+      :type loc: float or pdarray of floats, optional
+      :param scale: Standard deviation of the distribution. Must be non-negative. Default of 1.
+      :type scale: float or pdarray of floats, optional
+      :param size: Output shape. Default is None, in which case a single value is returned.
+      :type size: numeric_scalars, optional
+
+      .. rubric:: Notes
+
+      The probability density for the Gaussian distribution is:
+
+      .. math::
+         p(x) = \frac{1}{\sqrt{2\pi \sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}
+
+      where :math:`\mu` is the mean and :math:`\sigma` the standard deviation.
+      The square of the standard deviation, :math:`\sigma^2`, is called the variance.
+
+      :returns: Pdarray of floats (unless size=None, in which case a single float is returned).
+      :rtype: pdarray
+
+      .. seealso:: :obj:`standard_normal`, :obj:`uniform`
+
+      .. rubric:: Examples
+
+      >>> ak.random.default_rng(17).normal(3, 2.5, 10)
+      array([2.3673425816523577 4.0532529435624589 2.0598322696795694])
+
+
+
    .. py:method:: permutation(x)
 
       Randomly permute a sequence, or return a permuted range.
@@ -173,9 +206,12 @@ Package Contents
 
       .. rubric:: Notes
 
-      For random samples from :math:`N(\mu, \sigma^2)`, use:
+      For random samples from :math:`N(\mu, \sigma^2)`, either call `normal` or do:
+
+      .. math::
+          (\sigma \cdot \texttt{standard_normal}(size)) + \mu
 
-      ``(sigma * standard_normal(size)) + mu``
+      .. seealso:: :obj:`normal`
 
       .. rubric:: Examples
 

diff --git a/_sources/usage/random.rst.txt b/_sources/usage/random.rst.txt
@@ -17,10 +17,26 @@ To create a ``Generator`` use the ``default_rng`` constructor
 Features
 ==========
 
+choice
+---------
+.. autofunction:: arkouda.random.Generator.choice
+
 integers
 ---------
 .. autofunction:: arkouda.random.Generator.integers
 
+normal
+---------
+.. autofunction:: arkouda.random.Generator.normal
+
+permutation
+---------
+.. autofunction:: arkouda.random.Generator.permutation
+
+shuffle
+---------
+.. autofunction:: arkouda.random.Generator.shuffle
+
 random
 ---------
 .. autofunction:: arkouda.random.Generator.random