UPdated docs and version number

docs/README.rst

@@ -3,7 +3,7 @@ piecewise-regression (aka segmented regression) in python
 ==========================================================
 :piecewise-regression: fitting straight line models with breakpoints
 :Author: Charlie Pilgrim
-:Version: 1.4.3
+:Version: 1.5.0
 :Github: https://github.com/chasmani/piecewise-regression
 :Documentation: https://piecewise-regression.readthedocs.io/en/master/index.html
 :Paper: https://joss.theoj.org/papers/10.21105/joss.03859

docs/_build/html/README.html

@@ -81,7 +81,7 @@ <h1>piecewise-regression (aka segmented regression) in python<a class="headerlin
 <dd class="field-even"><p>Charlie Pilgrim</p>
 </dd>
 <dt class="field-odd">Version<span class="colon">:</span></dt>
-<dd class="field-odd"><p>1.4.3</p>
+<dd class="field-odd"><p>1.5.0</p>
 </dd>
 <dt class="field-even">Github<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://github.com/chasmani/piecewise-regression">https://github.com/chasmani/piecewise-regression</a></p>
@@ -216,7 +216,7 @@ <h2>How It Works<a class="headerlink" href="#how-it-works" title="Link to this h
 </section>
 <section id="model-selection">
 <h2>Model Selection<a class="headerlink" href="#model-selection" title="Link to this heading"></a></h2>
-<p>In addition to the main Fit tool, the package also offers a ModelSelection option based on the Bayesian Information Criterion (BIC). This additional tool is experimental and not as thorough as the main Fit function. In particular, the models are generated with random start_values, which can influence the model fit and give different values for the BIC. The tool can help explore other possible models but should not be used to choose the best model at this time.</p>
+<p>In addition to the main Fit tool, the package also offers a ModelSelection option based on the Bayesian Information Criterion (BIC). This additional tool is opinionated in it’s choices (e.g. using the BIC) and not as thorough as the main Fit function. In particular, the models are generated with random start_values, which can influence the model fit and give different values for the BIC. The tool can help explore other possible models but we recommend that caution and domain knowledge are used when interpreting the results.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ms</span> <span class="o">=</span> <span class="n">piecewise_regression</span><span class="o">.</span><span class="n">ModelSelection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">max_breakpoints</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
 </pre></div>
 </div>

docs/_build/html/_sources/README.rst.txt

@@ -3,7 +3,7 @@ piecewise-regression (aka segmented regression) in python
 ==========================================================
 :piecewise-regression: fitting straight line models with breakpoints
 :Author: Charlie Pilgrim
-:Version: 1.4.3
+:Version: 1.5.0
 :Github: https://github.com/chasmani/piecewise-regression
 :Documentation: https://piecewise-regression.readthedocs.io/en/master/index.html
 :Paper: https://joss.theoj.org/papers/10.21105/joss.03859
@@ -155,7 +155,7 @@ As is often the case with fitting non-linear models, even with these measures, t
 Model Selection
 ==========================
 
-In addition to the main Fit tool, the package also offers a ModelSelection option based on the Bayesian Information Criterion (BIC). This additional tool is experimental and not as thorough as the main Fit function. In particular, the models are generated with random start_values, which can influence the model fit and give different values for the BIC. The tool can help explore other possible models but should not be used to choose the best model at this time. ::
+In addition to the main Fit tool, the package also offers a ModelSelection option based on the Bayesian Information Criterion (BIC). This additional tool is opinionated in it's choices (e.g. using the BIC) and not as thorough as the main Fit function. In particular, the models are generated with random start_values, which can influence the model fit and give different values for the BIC. The tool can help explore other possible models but we recommend that caution and domain knowledge are used when interpreting the results.  ::
 
 	ms = piecewise_regression.ModelSelection(x, y, max_breakpoints=6)
 

docs/_build/html/api.html

@@ -60,6 +60,7 @@
 <li class="toctree-l4"><a class="reference internal" href="#piecewise_regression.main.Fit.plot_breakpoints"><code class="docutils literal notranslate"><span class="pre">Fit.plot_breakpoints()</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="#piecewise_regression.main.Fit.plot_data"><code class="docutils literal notranslate"><span class="pre">Fit.plot_data()</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="#piecewise_regression.main.Fit.plot_fit"><code class="docutils literal notranslate"><span class="pre">Fit.plot_fit()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#piecewise_regression.main.Fit.predict"><code class="docutils literal notranslate"><span class="pre">Fit.predict()</span></code></a></li>
 <li class="toctree-l4"><a class="reference internal" href="#piecewise_regression.main.Fit.summary"><code class="docutils literal notranslate"><span class="pre">Fit.summary()</span></code></a></li>
 </ul>
 </li>
@@ -269,6 +270,18 @@ <h2>Main<a class="headerlink" href="#main" title="Link to this heading"></a><
 Passes any kwargs to the matplotlib plot function, e.g. color=”red”.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="piecewise_regression.main.Fit.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xx_predict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#piecewise_regression.main.Fit.predict" title="Link to this definition"></a></dt>
+<dd><p>Predict y values from x values given the fitted model.
+Returns predictions as a list of numbers.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>xx</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>floats</em>) – Data series in x-axis.</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="piecewise_regression.main.Fit.summary">
 <span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#piecewise_regression.main.Fit.summary" title="Link to this definition"></a></dt>

docs/_build/html/genindex.html

@@ -214,6 +214,8 @@ <h2 id="P">P</h2>
       <li><a href="api.html#piecewise_regression.main.Fit.plot_data">plot_data() (piecewise_regression.main.Fit method)</a>
 </li>
       <li><a href="api.html#piecewise_regression.main.Fit.plot_fit">plot_fit() (piecewise_regression.main.Fit method)</a>
+</li>
+      <li><a href="api.html#piecewise_regression.main.Fit.predict">predict() (piecewise_regression.main.Fit method)</a>
 </li>
   </ul></td>
 </tr></table>

docs/_build/html/index.html

@@ -84,7 +84,7 @@ <h2>piecewise-regression (aka segmented regression) in python<a class="headerlin
 <dd class="field-even"><p>Charlie Pilgrim</p>
 </dd>
 <dt class="field-odd">Version<span class="colon">:</span></dt>
-<dd class="field-odd"><p>1.4.3</p>
+<dd class="field-odd"><p>1.5.0</p>
 </dd>
 <dt class="field-even">Github<span class="colon">:</span></dt>
 <dd class="field-even"><p><a class="reference external" href="https://github.com/chasmani/piecewise-regression">https://github.com/chasmani/piecewise-regression</a></p>
@@ -221,7 +221,7 @@ <h1>How It Works<a class="headerlink" href="#how-it-works" title="Link to this h
 </section>
 <section id="model-selection">
 <h1>Model Selection<a class="headerlink" href="#model-selection" title="Link to this heading"></a></h1>
-<p>In addition to the main Fit tool, the package also offers a ModelSelection option based on the Bayesian Information Criterion (BIC). This additional tool is experimental and not as thorough as the main Fit function. In particular, the models are generated with random start_values, which can influence the model fit and give different values for the BIC. The tool can help explore other possible models but should not be used to choose the best model at this time.</p>
+<p>In addition to the main Fit tool, the package also offers a ModelSelection option based on the Bayesian Information Criterion (BIC). This additional tool is opinionated in it’s choices (e.g. using the BIC) and not as thorough as the main Fit function. In particular, the models are generated with random start_values, which can influence the model fit and give different values for the BIC. The tool can help explore other possible models but we recommend that caution and domain knowledge are used when interpreting the results.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ms</span> <span class="o">=</span> <span class="n">piecewise_regression</span><span class="o">.</span><span class="n">ModelSelection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">max_breakpoints</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
 </pre></div>
 </div>

setup.py

@@ -3,7 +3,7 @@
 # This call to setup() does all the work
 setuptools.setup(
     name="piecewise-regression",
-    version="1.4.3",
+    version="1.5.0",
     description="piecewise (segmented) regression in python",
     long_description=   "piecewise-regression provides tools for fitting " 
                         "continuous straight line models to data with "