mkdocs source file edits minor format fixes.

docs/citing/index.html

@@ -120,22 +120,22 @@
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             <div class="section">
 
-                <p>If you use scikit-rebate or the MultiSURF algorithm in a scientific publication, please consider citing the following paper (currently available as a pre-print in arXiv):</p>
-<p>Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods." arXiv preprint arXiv:1711.08477 (2017).</p>
+                <p>If you use ReBATE or the MultiSURF algorithm in a scientific publication, please consider citing the following paper (currently available as a pre-print in arXiv):</p>
+<p><em>Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods." arXiv preprint arXiv:1711.08477 (2017).</em></p>
 <p>Alternatively a complete review of Relief-based algorithms is available at:</p>
-<p>Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." arXiv preprint arXiv:1711.08421 (2017).</p>
+<p><em>Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." arXiv preprint arXiv:1711.08421 (2017).</em></p>
 <p>To cite the original Relief paper:</p>
-<p>Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.</p>
+<p><em>Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.</em></p>
 <p>To cite the original ReliefF paper: </p>
-<p>Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.</p>
+<p><em>Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.</em></p>
 <p>To cite the original SURF paper:</p>
-<p>Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData mining 2, no. 1 (2009): 5.</p>
+<p><em>Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData mining 2, no. 1 (2009): 5.</em></p>
 <p>To cite the original SURF* paper: </p>
-<p>Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.</p>
+<p><em>Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.</em></p>
 <p>To cite the original MultiSURF* paper:</p>
-<p>Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.</p>
+<p><em>Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.</em></p>
 <p>To cite the original TuRF paper: </p>
-<p>Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.</p>
+<p><em>Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.</em></p>
 
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docs/index.html

@@ -199,5 +199,5 @@
 
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 MkDocs version : 0.17.3
-Build Date UTC : 2018-05-07 21:48:47
+Build Date UTC : 2018-05-07 21:56:17
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docs/releases/index.html

@@ -158,7 +158,7 @@ <h1 id="rebate-02">ReBATE 0.2</h1>
 </ul>
 <h1 id="rebate-01">ReBATE 0.1</h1>
 <ul>
-<li>Initial release of Relief algorithms, including ReliefF, SURF, SURF<em>, MultSURF</em>, and TuRF.</li>
+<li>Initial release of Relief algorithms, including ReliefF, SURF, SURF*, MultSURF*, and TuRF.</li>
 </ul>
 
             </div>

docs/using/index.html

@@ -193,7 +193,7 @@ <h2 id="available-rebate-parameters">Available ReBATE Parameters</h2>
 </tr>
 <tr>
 <td>-x, --testdata</td>
-<td>>String with path/name</td>
+<td>String with path/name</td>
 <td>Only used in conjunction with --topattr. When a file path/name is specified pointing to a testing dataset (generated by the user to pair with the given training dataset), ReBATE will also generate an output testing file that includes only the top attributes identified by the Relief-based algorithm scores on the training data. This is a convenience function.  </td>
 </tr>
 </table>

docs_sources/citing.md

@@ -1,31 +1,31 @@
-If you use scikit-rebate or the MultiSURF algorithm in a scientific publication, please consider citing the following paper (currently available as a pre-print in arXiv):
+If you use ReBATE or the MultiSURF algorithm in a scientific publication, please consider citing the following paper (currently available as a pre-print in arXiv):
 
-Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods." arXiv preprint arXiv:1711.08477 (2017).
+*Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods." arXiv preprint arXiv:1711.08477 (2017).*
 
 Alternatively a complete review of Relief-based algorithms is available at:
 
-Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." arXiv preprint arXiv:1711.08421 (2017).
+*Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." arXiv preprint arXiv:1711.08421 (2017).*
 
 To cite the original Relief paper:
 
-Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.
+*Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.*
 
 To cite the original ReliefF paper: 
 
-Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.
+*Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.*
 
 To cite the original SURF paper:
 
-Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData mining 2, no. 1 (2009): 5.
+*Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData mining 2, no. 1 (2009): 5.*
 
-To cite the original SURF* paper: 
+To cite the original SURF\* paper: 
 
-Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.
+*Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.*
 
-To cite the original MultiSURF* paper:
+To cite the original MultiSURF\* paper:
 
-Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.
+*Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.*
 
 To cite the original TuRF paper: 
 
-Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.
+*Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.*

docs_sources/releases.md

@@ -10,7 +10,7 @@
 
 * Fixed score normalizations so they fall between -1 and 1 for all algorithms Now matches scikit-rebate.
 
-* Consolidated MultiSURF* so that one script is used for both multiclass, and other types of endpoints. 
+* Consolidated MultiSURF\* so that one script is used for both multiclass, and other types of endpoints. 
 
 * Added an automatic (standard deviation based) ramp function method that is utilized by all algorithms on data with a mix of discrete and continuous features. Taken from scikit-rebate.
 
@@ -20,4 +20,4 @@
 
 # ReBATE 0.1
 
-* Initial release of Relief algorithms, including ReliefF, SURF, SURF*, MultSURF*, and TuRF.
+* Initial release of Relief algorithms, including ReliefF, SURF, SURF\*, MultSURF\*, and TuRF.

docs_sources/using.md

@@ -65,7 +65,7 @@ Below we review the run parameters that can be specified (depending on the Relie
 </tr>
 <tr>
 <td>-x, --testdata</td>
-<td>>String with path/name</td>
+<td>String with path/name</td>
 <td>Only used in conjunction with --topattr. When a file path/name is specified pointing to a testing dataset (generated by the user to pair with the given training dataset), ReBATE will also generate an output testing file that includes only the top attributes identified by the Relief-based algorithm scores on the training data. This is a convenience function.  </td>
 </tr>
 </table>