Package: diffee 1.1.0

diffee: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arxiv:1710.11223>.

Authors:Beilun Wang [aut, cre], Yanjun Qi [aut], Zhaoyang Wang [aut]

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diffee.pdf |diffee.html
diffee/json (API)

# Install 'diffee' in R:
install.packages('diffee', repos = c('https://blw921.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/qdata/diffee/issues

Datasets:
  • cancer - Microarray data set for breast cancer
  • exampleData - A simulated toy dataset that includes 2 data matrices (from 2 related tasks).
  • exampleData500 - A simulated toy dataset that includes 2 data matrices (from 2 related tasks).
  • exampleDataGraph - A simulated toy dataset that includes 3 igraph objects
  • nip_37_data - NIPS word count dataset

On CRAN:

1.20 score 16 scripts 136 downloads 3 exports 13 dependencies

Last updated 6 years agofrom:1d0efa7e93. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winOKNov 02 2024
R-4.5-linuxOKNov 02 2024
R-4.4-winOKNov 02 2024
R-4.4-macOKNov 02 2024
R-4.3-winOKNov 02 2024
R-4.3-macOKNov 02 2024

Exports:diffeeplot.diffeereturngraph

Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMatrixmvtnormpcaPPpkgconfigrlangvctrs