BANAM

The network autocorrelation model (NAM) can be used for studying the degree of social influence regarding an outcome variable based on one or more known networks. The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) doi:10.1016/j.socnet.2016.09.002 and Dittrich, Leenders, and Mulder (2019) doi:10.1177/0049124117729712 are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) doi:10.1177/0081175020913899 are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) doi:10.1111/bmsp.12110 is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input.

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