# Inferring metabolic networks from untargeted high-resolution mass spectrometry data

## Documentation for package ‘MetNet’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

MetNet-package | Inferring metabolic networks from untargeted high-resolution mass spectrometry data
addToList | Add adjacency matrix to list
aracne | Create an adjacency matrix based on algorithm for the reconstruction of accurate cellular networks
bayes | Create an adjacency matrix based on constraint-based structure learning algorithm
clr | Create an adjacency matrix based on context likelihood or relatedness network
combineStructuralStatistical | Combine structural and statistical adjacency matrix
consensusAdjacency | Create a consensus adjacency matrix of statistical adjacency matrices
correlation | Create an adjacency matrix based on correlation
createStatisticalAdjacency | Create statistical adjacency matrix
createStatisticalAdjacencyList | Create a list of statistical adjacency matrices
createStructuralAdjacency | Create adjacency matrix based on m/z (molecular weight) difference
lasso | Create a adjacency matrix based on LASSO
mat_test | Example data for 'MetNet': unit tests
mat_test_z | Example data for 'MetNet': unit tests
MetNet | Inferring metabolic networks from untargeted high-resolution mass spectrometry data
peaklist | Example data for 'MetNet': data input
randomForest | Create a adjacency matrix based on random forest
rtCorrection | Correct connections in the structural adjacency matrix by retention time
threeDotsCall | Check if passed arguments match the function's formal arguments and call the function with the checked arguments
x_test | Example data for 'MetNet': data input