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multiplatform-runblocking-native from group com.baremetalcloud (version 1.1)

Multiplatform runBlocking workaround for testing found here. [https://github.com/Kotlin/kotlinx.coroutines/issues/1996](https://github.com/Kotlin/kotlinx.coroutines/issues/1996)

Group: com.baremetalcloud Artifact: multiplatform-runblocking-native
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Artifact multiplatform-runblocking-native
Group com.baremetalcloud
Version 1.1
Last update 21. May 2021
Organization not specified
URL https://github.com/baremetalcloud/multiplatform-runblocking
License The Apache Software License, Version 2.0
Dependencies amount 3
Dependencies kotlin-stdlib-common, kotlinx-coroutines-core-linuxx64, multiplatform-file-native,
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multiplatform-runblocking from group com.baremetalcloud (version 1.1)

Multiplatform runBlocking workaround for testing found here. [https://github.com/Kotlin/kotlinx.coroutines/issues/1996](https://github.com/Kotlin/kotlinx.coroutines/issues/1996)

Group: com.baremetalcloud Artifact: multiplatform-runblocking
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Artifact multiplatform-runblocking
Group com.baremetalcloud
Version 1.1
Last update 21. May 2021
Organization not specified
URL https://github.com/baremetalcloud/multiplatform-runblocking
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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multiplatform-runblocking-jvm from group com.baremetalcloud (version 1.1)

Multiplatform runBlocking workaround for testing found here. [https://github.com/Kotlin/kotlinx.coroutines/issues/1996](https://github.com/Kotlin/kotlinx.coroutines/issues/1996)

Group: com.baremetalcloud Artifact: multiplatform-runblocking-jvm
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Artifact multiplatform-runblocking-jvm
Group com.baremetalcloud
Version 1.1
Last update 21. May 2021
Organization not specified
URL https://github.com/baremetalcloud/multiplatform-runblocking
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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multiplatform-runblocking-js from group com.baremetalcloud (version 1.1)

Multiplatform runBlocking workaround for testing found here. [https://github.com/Kotlin/kotlinx.coroutines/issues/1996](https://github.com/Kotlin/kotlinx.coroutines/issues/1996)

Group: com.baremetalcloud Artifact: multiplatform-runblocking-js
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Artifact multiplatform-runblocking-js
Group com.baremetalcloud
Version 1.1
Last update 21. May 2021
Organization not specified
URL https://github.com/baremetalcloud/multiplatform-runblocking
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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pure-uuid-java from group com.graphqlio (version 0.0.13)

This is a pure Java library for the generation of DCE 1.1, ISO/IEC 11578:1996 and IETF RFC-4122 compliant Universally Unique Identifier (UUID). It supports DCE 1.1 variant UUIDs of version 1 (time and node based), version 3 (name based, MD5), version 4 (random number based) and version 5 (name based, SHA-1). The library is part of the graphqlio ecosystem.

Group: com.graphqlio Artifact: pure-uuid-java
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Artifact pure-uuid-java
Group com.graphqlio
Version 0.0.13
Last update 12. October 2020
Organization not specified
URL http://graphqlio.com
License MIT License
Dependencies amount 1
Dependencies commons-lang,
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naiveBayesTree from group nz.ac.waikato.cms.weka (version 1.0.2)

Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.

Group: nz.ac.waikato.cms.weka Artifact: naiveBayesTree
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Artifact naiveBayesTree
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/naiveBayesTree
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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optics_dbScan from group nz.ac.waikato.cms.weka (version 1.0.6)

The OPTICS and DBScan clustering algorithms. Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996; Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD International Conference on Management of Data, 49-60, 1999.

Group: nz.ac.waikato.cms.weka Artifact: optics_dbScan
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Artifact optics_dbScan
Group nz.ac.waikato.cms.weka
Version 1.0.6
Last update 16. December 2019
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/optics_dbScan
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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generalizedSequentialPatterns from group nz.ac.waikato.cms.weka (version 1.0.2)

Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences contained in the set can be determined by the respective option. Furthermore, the set of output results can be restricted by specifying one or more attributes that have to be contained in each element/itemset of a sequence. For further information see: Ramakrishnan Srikant, Rakesh Agrawal (1996). Mining Sequential Patterns: Generalizations and Performance Improvements.

Group: nz.ac.waikato.cms.weka Artifact: generalizedSequentialPatterns
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Artifact generalizedSequentialPatterns
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/generalizedSequentialPatterns
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

consistencySubsetEval from group nz.ac.waikato.cms.weka (version 1.0.4)

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.

Group: nz.ac.waikato.cms.weka Artifact: consistencySubsetEval
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1 downloads

Artifact consistencySubsetEval
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 16. October 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/consistencySubsetEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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