<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
  <channel>
    <title>PyPI recent updates for PyNomaly</title>
    <link>https://pypi.org/project/pynomaly/</link>
    <description>Recent updates to the Python Package Index for PyNomaly</description>
    <language>en</language>    <item>
      <title>0.4.0</title>
      <link>https://pypi.org/project/pynomaly/0.4.0/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Tue, 26 May 2026 18:45:51 GMT</pubDate>
    </item>    <item>
      <title>0.3.5</title>
      <link>https://pypi.org/project/pynomaly/0.3.5/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Wed, 04 Feb 2026 22:32:08 GMT</pubDate>
    </item>    <item>
      <title>0.3.4</title>
      <link>https://pypi.org/project/pynomaly/0.3.4/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Fri, 18 Oct 2024 15:45:30 GMT</pubDate>
    </item>    <item>
      <title>0.3.3</title>
      <link>https://pypi.org/project/pynomaly/0.3.3/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sat, 30 May 2020 15:58:20 GMT</pubDate>
    </item>    <item>
      <title>0.3.2</title>
      <link>https://pypi.org/project/pynomaly/0.3.2/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sun, 20 Oct 2019 23:02:52 GMT</pubDate>
    </item>    <item>
      <title>0.3.1</title>
      <link>https://pypi.org/project/pynomaly/0.3.1/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Mon, 05 Aug 2019 02:47:01 GMT</pubDate>
    </item>    <item>
      <title>0.3.0</title>
      <link>https://pypi.org/project/pynomaly/0.3.0/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 20 Jun 2019 07:00:19 GMT</pubDate>
    </item>    <item>
      <title>0.2.7</title>
      <link>https://pypi.org/project/pynomaly/0.2.7/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Tue, 09 Apr 2019 21:30:42 GMT</pubDate>
    </item>    <item>
      <title>0.2.6</title>
      <link>https://pypi.org/project/pynomaly/0.2.6/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 03 Jan 2019 16:23:23 GMT</pubDate>
    </item>    <item>
      <title>0.2.5</title>
      <link>https://pypi.org/project/pynomaly/0.2.5/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 22 Nov 2018 13:04:40 GMT</pubDate>
    </item>    <item>
      <title>0.2.4</title>
      <link>https://pypi.org/project/pynomaly/0.2.4/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sat, 27 Oct 2018 01:41:45 GMT</pubDate>
    </item>    <item>
      <title>0.2.3</title>
      <link>https://pypi.org/project/pynomaly/0.2.3/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 30 Aug 2018 04:33:57 GMT</pubDate>
    </item>    <item>
      <title>0.2.2</title>
      <link>https://pypi.org/project/pynomaly/0.2.2/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 30 Aug 2018 03:53:00 GMT</pubDate>
    </item>    <item>
      <title>0.2.1</title>
      <link>https://pypi.org/project/pynomaly/0.2.1/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sat, 26 May 2018 00:50:42 GMT</pubDate>
    </item>    <item>
      <title>0.2.0</title>
      <link>https://pypi.org/project/pynomaly/0.2.0/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Mon, 30 Apr 2018 19:39:20 GMT</pubDate>
    </item>    <item>
      <title>0.1.8</title>
      <link>https://pypi.org/project/pynomaly/0.1.8/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Tue, 03 Apr 2018 03:09:53 GMT</pubDate>
    </item>    <item>
      <title>0.1.7</title>
      <link>https://pypi.org/project/pynomaly/0.1.7/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 21 Dec 2017 06:46:36 GMT</pubDate>
    </item>    <item>
      <title>0.1.6</title>
      <link>https://pypi.org/project/pynomaly/0.1.6/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sun, 17 Dec 2017 18:47:56 GMT</pubDate>
    </item>    <item>
      <title>0.1.5</title>
      <link>https://pypi.org/project/pynomaly/0.1.5/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sun, 30 Jul 2017 04:04:24 GMT</pubDate>
    </item>    <item>
      <title>0.1.4</title>
      <link>https://pypi.org/project/pynomaly/0.1.4/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 29 Jun 2017 06:26:52 GMT</pubDate>
    </item>    <item>
      <title>0.1.3</title>
      <link>https://pypi.org/project/pynomaly/0.1.3/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Sat, 10 Jun 2017 23:21:21 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/pynomaly/0.1.2/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Thu, 01 Jun 2017 16:58:33 GMT</pubDate>
    </item>    <item>
      <title>0.1.0</title>
      <link>https://pypi.org/project/pynomaly/0.1.0/</link>
      <description>A Python 3 implementation of LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].</description>
<author>vc@valentino.io</author>      <pubDate>Wed, 31 May 2017 02:57:51 GMT</pubDate>
    </item>  </channel>
</rss>