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9 projects in result set.
0. CITAN - CITAN is a library of functions useful in - but not limited to - quantitative research in the field of scientometrics.
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Registered: 2011-04-24 10:39 |
1. likelihood-based image id code - Computer-assisted method for assigning animal ids to a database of images using phenotypic measurements extracted from each image. Method is based on likelihood theory, and can handle multiple sources of error inherit in image identification.
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Registered: 2013-03-15 14:51 |
2. track: store obects on disk - Automatically stores objects in files on disk so that
files are rewritten when objects are changed, and so
that objects are accessible but do not occupy memory until
they are accessed. Also tracks times when objects are created
and modified.
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Registered: 2010-06-22 04:40 |
3. Plot manageable subsets of data - Plotting a large data set is difficult if it does not fit into memory as a data frame, so we introduce more efficient data structures and alternative plot functions that work with large data.
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- Development Status : 3 - Alpha [Filter]
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Registered: 2009-09-29 11:02 |
4. aroma.* packages [MOVED TO GITHUB] - This repository and all of its packages have been moved to https://github.com/HenrikBengtsson/
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Registered: 2009-05-19 20:01 |
5. dcGOR - dcGOR is a package for analysing domain-centric ontologies and annotations. It includes: enrichment analysis and visualisation; construction of a domain (semantic similarity) network; significance analysis for a contact network.
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Registered: 2014-10-23 15:21 |
6. RODBC Extension - Provides functions for packages that require enhanced database connectivity by exploiting RODBC functions combined with basic R functions and (hopefully) other packages as well.
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Registered: 2009-02-23 06:54 |
7. XGR - eXploring Genomic Relations (XGR) at the gene and SNP level through enrichment analysis, similarity analysis and network analysis
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Registered: 2016-04-01 13:46 |
8. Genotype Database Framework - GT.DB is a database framework for efficiently managing genome-wide genotype datasets (i.e. 10^6 or more biallelic markers, 1000s of samples), as well as underlying raw data and complex phenotype data, tightly integrated with R libraries for analysis.
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Registered: 2009-09-17 00:13 |
