Inferring metabolic networks from untargeted high-resolution mass spectrometry data

Documentation for package ‘MetNet’ version 1.2.0

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