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) .


Reference manual

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1.3.0 by Beilun Wang, a year ago


Report a bug at https://github.com/QData/SIMULE

Browse source code at https://github.com/cran/simule

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

Documentation:   PDF Manual  

GPL-2 license

Depends on lpSolve, pcaPP, igraph

Suggests parallel

See at CRAN