Search Results for "package installer linux"

Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples.

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. License: The DSS R-package is made available under the terms of the GNU General Public License, which means that the source code is freely available for use within other software, but if you alter the code and distribute it, you must make the new source code...

An open-source implementation of our network-based target deconvolution approach, named target addiction score (TAS). License: The TAS R-package is made available under the terms of the GNU General Public License, which means that the source code is freely available for use within other software, but if you alter the code and distribute it, you must make the new source code freely available as well. This software is distributed in the hope that it will be useful, but WITHOUT ANY...

GISBox by Citiviz is a ready-to-use virtual appliance containing an assortment of powerful, free and open source GIS, Data Mining and Analysis, Modelling and Programming software and packages. The pre-configured and tested GISBox allows you to try and use in few minutes a variety of software used by data scientists, engineers, physicists, mathematicians, geomatics engineers, and geographers from around the globe, without installing anything more than once the Oracle VirtualBox™ software...

A simple tool to help you create an R interface for your Java project. Simply annotate the constructors and methods you want to make available and JAnnotateR will produce a working package, complete with documentation generated from javadocs.

An R package implementation of a consensus clustering methodology. This package allows users to perform re-sampling statistics based clustering using multiple clustering algorithms to assess the robustness of both clusters and members of clusters.