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                  <text>VOLUME 104 Tahun 2022</text>
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                <text>Statistical Network Analysis with Bergm</text>
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                <text> Bayesian inference, exponential random graph models, R packages.</text>
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                <text>Recent advances in computational methods for intractable models have made network&#13;
data increasingly amenable to statistical analysis. Exponential random graph models&#13;
(ERGMs) emerged as one of the main families of models capable of capturing the complex&#13;
dependence structure of network data in a wide range of applied contexts. The Bergm&#13;
package for R has become a popular package to carry out Bayesian parameter inference,&#13;
missing data imputation, model selection and goodness-of-fit diagnostics for ERGMs.&#13;
Over the last few years, the package has been considerably improved in terms of efficiency&#13;
by adopting some of the state-of-the-art Bayesian computational methods for doublyintractable distributions. Recently, version 5 of the package has been made available on&#13;
CRAN having undergone a substantial makeover, which has made it more accessible and&#13;
easy to use for practitioners. New functions include data augmentation procedures based&#13;
on the approximate exchange algorithm for dealing with missing data, adjusted pseudolikelihood and pseudo-posterior procedures, which allow for fast approximate inference of&#13;
the ERGM parameter posterior and model evidence for networks on several thousands&#13;
nodes</text>
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                <text>Alberto Caimo</text>
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                <text>https://www.jstatsoft.org/article/view/v104i01</text>
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                <text>TU Dublin</text>
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                <text>September 2022</text>
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                <text>Fajar bagus W</text>
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