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>.

Version: 1.3.0
Depends: R (≥ 3.0.0), lpSolve, pcaPP, igraph
Suggests: parallel
Published: 2018-07-02
DOI: 10.32614/CRAN.package.simule
Author: Beilun Wang [aut, cre], Yanjun Qi [aut], Zhaoyang Wang [aut]
Maintainer: Beilun Wang <bw4mw at>
License: GPL-2
NeedsCompilation: no
CRAN checks: simule results


Reference manual: simule.pdf


Package source: simule_1.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): simule_1.3.0.tgz, r-oldrel (arm64): simule_1.3.0.tgz, r-release (x86_64): simule_1.3.0.tgz, r-oldrel (x86_64): simule_1.3.0.tgz
Old sources: simule archive


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