gclm: Graphical Continuous Lyapunov Models (GCLM)

In rlebron-bioinfo/gnlearn: Genetic Network Learning

Description

This function allows you to learn a directed graph from a dataset using the GCLM algorithm.

Usage

gclm(
  df,
  lambda = 0.1,
  m = NULL,
  to = c("igraph", "adjacency", "edges", "graph", "bnlearn"),
  seed = sample(1:10^6, 1)
)

Arguments

df	Dataset.
lambda	Lambda regularization parameter. Use NULL to compute lambda. Default: 0.1
m	Size of training set (optional). Default: nrow(df)/2
to	Output format ('adjacency', 'edges', 'graph', 'igraph', or 'bnlearn') (optional).
seed	Seed used for random selection. Default: NULL

Examples

g <- gclm(df)

rlebron-bioinfo/gnlearn documentation built on July 25, 2020, 12:38 p.m.