Result for 101F855367AC6956C780F17E505ACFD6A00CDDEC

Query result

Key Value
FileName./usr/lib64/R/library/graphsim/R/graphsim.rdb
FileSize63044
MD533183A64CCD1AC55CB37496F7D12F4A3
SHA-1101F855367AC6956C780F17E505ACFD6A00CDDEC
SHA-2569E71A085B32B6286837BC5DF40631DB8F1D94E72350902D0B037AA3A5E9D795C
SSDEEP1536:z8K83XaAWoFIitX9HxdWvgr/YDnbGthcVQp/VQpE:r9oeItRdxYWth8QDQa
TLSHT1DC5301BDB3ACE16A89036C6DED42D32AB07150E85B909BA430A6915CCFECF142E15BD1
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD50C2439F973AB6D7D9984044D9286F1B4
PackageArchx86_64
PackageDescriptionFunctions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.
PackageNameR-graphsim
PackageReleaselp153.1.2
PackageVersion1.0.2
SHA-1849B5CAD637A371B66D037F41D88B3123B9307E3
SHA-2562FECEEAFE5441BB50D67ADDD47B1495F613340D694E04E8C6B5C8EFFBC9B75E9