Python wrapper for sparseSEM
Project description
Elastic Net for Structural Equation Models (SEM)
Anhui Huang | Ph.D. Electrical and Computer Engineering
https://scholar.google.com/citations?user=WhDMZEIAAAAJ&hl=en
Online Documentation
The theory and background for network topology inference using sparse Structural Equation Models (SEM) can be found in my Ph.D dissertation (Huang A. 2014). The experimental study are also available in the documentation in the package.
Configuration
This package was originally developed to leverage high performance computer clusters to enable parallel computation through openMPI. Users who have access to large scale computational resources can explore the functionality and checkout the openMPI module in this package.
Current package utilizes blas/lapack for high speed computation. To build the C/C++ code, the intel OneMKL library is specified in the package setup.
- Install the free OneMKL package (https://www.intel.com/content/www/us/en/docs/oneapi/programming-guide/2023-0/intel-oneapi-math-kernel-library-onemkl.html)
- Check if your package is the same as in the setup.py file ('/opt/intel/oneapi/mkl/2023.1.0/include'). Update the file accordingly if it was installed in a different path.
Reference
- Huang A. (2014) Sparse Model Learning for Inferring Genotype and Phenotype Associations. Ph.D Dissertation,
University of Miami, Coral Gables, FL, USA.
- Huang A. (2014) sparseSEM: Sparse-Aware Maximum Likelihood for Structural Equation Models. Rpackage
(https://cran.r-project.org/web/packages/sparseSEM/index.html)
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