This page contains nightly builds of Orange from the code repository. These are typically stable and we recommend using them.
Full package: Snapshot of Orange with Python 3 and required libraries
This package is recommended to those installing Orange for the first time. It includes all required libraries (Python, PythonWin, NumPy, PyQt, PyQwt ...), though it will not change any libraries you might already have.
Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.
In Orange Canvas the analyst connects basic computational units, called widgets, into data flow analytics schemas. Two units-widgets can be connected if they share a data type. Compared to other popular tools like Taverna, Orange widgets are high-level, integrated potentially complex tasks, but are specific enough to be used independently. Even elaborate analyses rarely consist of more than ten widgets; while tasks such as clustering and enrichment analysis could be executed with up to five widgets. While building the schema each widget is independently controlled with settings, the settings do not conceptually burden the analyst.
Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.
This is a data fusion add-on for [Orange3](http://orange.biolab.si). Add-on wraps [scikit-fusion](http://github.com/marinkaz/scikit-fusion), a Python library for data fusion, and implements a set of widgets for loading of the data, definition of data fusion schema, collective matrix factorization and exploration of latent factors.Installation
To install the add-on, run
python setup.py install
To register this add-on with Orange, but keep the code in the development directory (do not copy it to Python's site-packages directory), run
python setup.py developUsage
Run Orange from the terminal by
python -m Orange.canvas
Data fusion widgets are in the toolbox bar under Data Fusion section.
Orange3 Text extends [Orange3](http://orange.biolab.si), a data mining software package, with common functionality for text mining. It provides access to publicly available data, like NY Times, Twitter and PubMed. Further, it provides tools for preprocessing, constructing vector spaces (like bag-of-words, topic modeling and word2vec) and visualizations like word cloud end geo map. All features can be combined with powerful data mining techniques from the Orange data mining framework.
Add-ons should be installed in Orange canvas (menu Options, Add-ons...).