https://github.com/Ivis4ml/fssemR

An optimizer of Fused-Sparse Structural Equation Models, which is the state-of-the-art (sota) jointly fused sparse maximum likelihood function for structural equation models proposed by Xin Zhou and Xiaodong Cai
https://github.com/Ivis4ml/fssemR

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An optimizer of Fused-Sparse Structural Equation Models, which is the state-of-the-art (sota) jointly fused sparse maximum likelihood function for structural equation models proposed by Xin Zhou and Xiaodong Cai

• Host: GitHub
• URL: https://github.com/Ivis4ml/fssemR
• Owner: Ivis4ml
• Created: 2019-03-12T02:01:40.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-04-01T21:00:20.000Z (over 2 years ago)
• Last Synced: 2023-11-20T16:03:27.632Z (about 1 year ago)
• Language: R
• Homepage:
• Size: 751 KB
• Stars: 4
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## fssemR

[![CRAN status](https://www.r-pkg.org/badges/version/fssemR)](https://cran.r-project.org/package=fssemR)

[![CRAN download](https://cranlogs.r-pkg.org/badges/fssemR)](https://cranlogs.r-pkg.org/badges/fssemR)

`fssemR` is a package that ultilizes the Proximal Alternating Linearized Maximal to solve the 

non-convex non-smooth jointly fused sparse structrual equation model. 

## Installation

`fssemR` package contains a lot of necessary scripts to analyze large dataset such as microarray and SNP data

from GEO database, so it has not been submitted to CRAN yet for these non-standard directory.

To install `fssemR`, you need a C++ compiler such as `g++` or `clang++` with C++11 feature,

and for Windows users, the [Rtools](https://cran.r-project.org/bin/windows/Rtools/index.html)

software is needed (unless you can configure the toolchain by yourself).

The installation follows the typical way of R packages on Github:

```r

library(devtools)

install_github("Ivis4ml/fssemR")

```

Now, `fssemR` package is uploaded on CRAN. So you will install it via CRAN 

```r

install.packages("fssemR")

```

## Vignette

[fssemR-introduction](https://github.com/Ivis4ml/fssemR/blob/master/inst/doc/fssemR.pdf)

## Citation

Xin Zhou, Xiaodong Cai, Inference of differential gene regulatory networks based on gene expression and genetic perturbation data, Bioinformatics, , btz529, https://doi.org/10.1093/bioinformatics/btz529

## Update

Upgrade to Eigen 0.3.4 compatible

## Acknowledgement

Thank should go to Yilun Zhang <[email protected]>/ for his contribution to the optimization for this package in math and implementation.