est.net  R Documentation 
This function estimates sparse networks using nonnegative matrix factorization (NMF) for data between change points.
est.net( Y, lambda, nruns = 50, rank = "optimal", algtype = "brunet", changepoints = NULL )
Y 
An input multivariate time series in matrix format, with variables organized in columns and time points in rows. All entries in Y must be positive. 
lambda 
A positive real number, which defines the clustering method and/or the cutoff value when estimating an adjacency matrix from the computed consensus matrix. If lambda = a positive integer value, say 6, completelinkage, hierarchical clustering is applied to the consensus matrix and the cutoff is at 6 clusters. If lambda is a vector of positive integer values, say c(4, 5, 6), the same clustering method is applied for each value sequentially. If lambda = a positive real number, say 0.5, entries in the consensus matrix with a value greater than or equal to 0.5 are labeled 1, while entries less than 0.5 are labeled 0. Similarly, if lambda is a vector of positive real numbers, say c(0.1, 0.3, 0.8), the same thresholding method is applied for each value sequentially. 
nruns 
A positive integer with default value equal to 50. It is used to define the number of runs in the NMF function. 
rank 
A character string or a positive integer, which defines the rank used in the optimization procedure to detect the change points. If rank = "optimal", which is also the default value, then the optimal rank is used. If rank = a positive integer value, say 4, then a predetermined rank is used. 
algtype 
A character string, which defines the algorithm to be used in the NMF function. By default it is set to "brunet". See the "Algorithms" section of

changepoints 
A vector of positive integers with default value equal to 
A matrix (or more specifically, an adjacency matrix) denoting the network (or clustering) structure between components of Y. If lambda is a vector, a list of adjacency matrices is returned, where each element of the list corresponds to an element in lambda.
Martin Ondrus, mondrus@ualberta.ca, Ivor Cribben, cribben@ualberta.ca
"Factorized Binary Search: a novel technique for change point detection in multivariate highdimensional time series networks", Ondrus et al. (2021), <arXiv:2103.06347>.
## Estimating the network for a multivariate data set, "sim2" with the settings: ## nruns = 10 and lambda = 0.5 where the latter specifies the cutoff based method est.net(sim2, lambda = 0.5, nruns = 4)
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