Package: simule 1.3.0
simule: A Constrained L1 Minimization Approach for Estimating Multiple Sparse Gaussian or Nonparanormal Graphical Models
This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <doi:10.1007/s10994-017-5635-7>.
Authors:
simule_1.3.0.tar.gz
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simule_1.3.0.tgz(r-4.4-any)simule_1.3.0.tgz(r-4.3-any)
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simule.pdf |simule.html✨
simule/json (API)
# Install 'simule' in R: |
install.packages('simule', repos = c('https://blw921.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/qdata/simule/issues
- cancer - Microarray data set for breast cancer
- exampleData - 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
Last updated 6 years agofrom:aa8fabf8cc. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win | NOTE | Nov 21 2024 |
R-4.5-linux | NOTE | Nov 21 2024 |
R-4.4-win | NOTE | Nov 21 2024 |
R-4.4-mac | NOTE | Nov 21 2024 |
R-4.3-win | OK | Nov 21 2024 |
R-4.3-mac | OK | Nov 21 2024 |
Exports:returngraphsimulewsimule
Dependencies:clicpp11glueigraphlatticelifecyclelpSolvemagrittrMatrixmvtnormpcaPPpkgconfigrlangvctrs