BANAM: Bayesian Analysis of the Network Autocorrelation Model
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.
| Version: |
0.2.2 |
| Depends: |
R (≥ 3.0.0), BFpack |
| Imports: |
Matrix, extraDistr, matrixcalc, mvtnorm, rARPACK, tmvtnorm, utils, psych, sna, bain |
| Suggests: |
testthat |
| Published: |
2024-12-03 |
| DOI: |
10.32614/CRAN.package.BANAM |
| Author: |
Joris Mulder [aut, cre],
Dino Dittrich [aut, ctb],
Roger Leenders [aut, ctb] |
| Maintainer: |
Joris Mulder <j.mulder3 at tilburguniversity.edu> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
BANAM results |
Documentation:
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