Reference¶

Documentation for available functions and arguments for those functions are provided here.

Estimators¶

NetworkTMLE(network, exposure, outcome[, ...])

Implementation of the Targeted Maximum Likelihood Estimator (TMLE) for network dependent data.

`uniform_network`(n, degree[, pr_w, seed])	Generates a uniform random graph for a set number of nodes (n) and specified max and min degree (degree).
`clustered_power_law_network`(n_cluster[, ...])	Generate a graph with the following features: follows a power-law degree distribution, high(er) clustering coefficient, and an underlying community structure.
`generate_observed`(graph[, seed])	Simulates the exposure and outcome for the uniform random graph (following mechanisms are from Sofrygin & van der Laan 2017).
`generate_truth`(graph, p)	Simulates the true conditional mean outcome for a given network, distribution of W, and policy.

Plan to add some basic utilities in a future version (such as helping setup the network as NetworkTMLE expects).