Search Results for "statistical analysis"
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Showing 5 open source projects for "statistical analysis"
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gretl
A cross-platform statistical package for econometric analysis
gretl is a cross-platform software package for econometric analysis, written in the C programming language. -
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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. -
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TXM
Unicode XML TEI text analysis platform
TXM is a free and open-source cross-platform Unicode & XML based text analysis environment and graphical client, supporting Windows, Linux and Mac OS X. It can also be used online as a J2EE standard compliant web portal (GWT based) with access control built in. DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP full text search engine (http://cwb.sourceforge.net) and a range of statistical functions (factorial analysis, classification, cooccurrency analysis, etc.) based on R packages (http://www.r-project.org). ... -
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KMWin
Kaplan-Meier for Windows
KMWin (Kaplan-Meier for Windows) is a convenient tool for graphical presentation of results from Kaplan-Meier survival time analysis. The programme is based on the statistical software environment R and provides an easy to use graphical interface. As an introduction, see http://dx.plos.org/10.1371/journal.pone.0038960#s2. -
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5
libcomposition
A C library to handle compositional (=closed) data, like proportions.
Inspired by Aitchison 2003 (http://www.amazon.com/The-Statistical-Analysis-Compositional-Data/dp/1930665784/) this library aims to make compositional data analysis rigorously.
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