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BANAM
View on CRAN: Click
here
Download and install BANAM package within the R console
Install from CRAN:
install.packages("BANAM")
Install from Github:
library("remotes")
install_github("cran/BANAM")
Install by package version:
library("remotes")
install_version("BANAM", "0.2.2")
Attach the package and use:
library("BANAM")
Maintained by
Joris Mulder
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[Scholar Profile | Author Map]
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First Published: 2024-05-29
Latest Update: 2024-05-29
Description:
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.
How to cite:
Joris Mulder (2024). BANAM: Bayesian Analysis of the Network Autocorrelation Model. R package version 0.2.2, https://cran.r-project.org/web/packages/BANAM. Accessed 21 Feb. 2025.
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