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Apache Spatial Information System (SIS) is a free software, Java language library for developing geospatial applications. SIS provides data structures for geographic features and associated metadata along with methods to manipulate those data structures. The library is an implementation of GeoAPI 3.0.2 interfaces and can be used for desktop or server applications. The SIS metadata module forms the base of the library and enables the creation of metadata objects which comply with the ISO 19115 international standard. The SIS referencing module enable the construction of geodetic data structures for geospatial referencing as defined by ISO 19111 standard, along with the associated operations which enable the transformation of coordinates between different reference systems. The SIS storage modules provide a common approach to the reading and writing of metadata, features and coverages applicable to simple imagery as to many dimensional data structures.

R can use RJava or jsr223 to communicate with java. R also has a class system called R6. If you want to use a java library with native rJava or jsr223 in R there is potentially a lot of glue code needed, and R library specific packaging configuration required. However if you don't mind writing an R-centric API in Java you can generate all of this glue code using a few java annotations and the normal javadoc annotations. This plugin aims to provide an annotation processor that writes that glue code and creates a fairly transparent connection between Java code and R code, with a minimum of hard work. The focus of this is streamlining the creation of R libraries by Java developers, rather than allowing access to arbitrary Java code from R. The ultimate aim of this plugin to allow java developers to provide simple APIs for their libraries, package their library using Maven, push it to github and for that to become seamlessly available as an R library, with a minimal amount of fuss. A focus is on trying to produce CI ready libraries tested with Github workflows and ready for CRAN submission.

Modelant provides JMI-compliant mechanism to read, process, export models in MOF 1.4, UML 1.3 and UML 1.4, including also an engine for generation of code (any texts) using Object-Oriented Programming and templates. It is an extensible framework allowing adding support of other languages, described using MOF 1.4 metamodels. Modelant provides Maven plugins to automate the creation and comparison of UML 1.3, UML 1.4, MOF 1.4 models; the generation of code from models; the reverse engineering of java code, relational databases and XML DTD/XSD; the transformatoion of models, e.g. tranformation of of UMl 1.3 models to UML 1.4; the comparison and change identification in UML 1.3/UML 1.4 models and MOF 1.4 metamodels. The produced UML 1.3 models are suitable to be imported in Rational Rose 2003 + XMI Plugin, WhiteStar UML / Star UML, Enterprise Architect, other, as they employ MOF 1.3/MOF 1.4 as reading MOF 1.3 is internally converted to MOF1.4 and XMI 1.1 and XMI 1.2. whereas the converted to UML 1.4 models can be imported in Argo UML, Enterprise Architect, other as they employ MOF 1.4 and XMI 1.1 and XMI 1.2. Modelant wraps the NetBeans Meta-Data Repository (MDR).

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extensible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

OpenIMAJ (Open Intelligent Multimedia in Java) is a collection of libraries and tools for multimedia analysis written in the Java programming language. OpenIMAJ intends to be the first truly complete multimedia analysis library and contains modules for analysing images, videos, text, audio and even webpages. The OpenIMAJ image and video analysis and feature extraction modules contain methods for processing visual content and extracting state-of-the-art features, including SIFT. The OpenIMAJ clustering and nearest-neighbour libraries contain efficient, multi-threaded implementations of clustering algorithms including Hierarchical K-Means and Approximate K-Means. The clustering library makes it possible to easily create visual-bag-of-words representations for images and video with very large vocabularies. The text-analysis modules contain implementations of a statistical language classifier and low-level processing pipeline. A number of modules deal with content creation, including interactive slideshows and animations. The hardware integration modules allow cross-platform integration with devices including webcams, the Microsoft Kinect, and even devices such as GPS's. OpenIMAJ also incorporates a number of tools to enable extremely-large-scale multimedia analysis using a distributed computing approach based on Apache Hadoop.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.