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VIMuRe

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Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data.

If you use this code, please cite this article:

Caterina De Bacco, Martina Contisciani, Jonathan Cardoso-Silva, Hadiseh Safdari, Gabriela Lima Borges, Diego Baptista, Tracy Sweet, Jean-Gabriel Young, Jeremy Koster, Cody T Ross, Richard McElreath, Daniel Redhead, Eleanor A Power, Latent network models to account for noisy, multiply reported social network data, Journal of the Royal Statistical Society Series A: Statistics in Society, 2023;, qnac004, https://doi.org/10.1093/jrsssa/qnac004

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VIMuRe package is available in R and Python. Check out the 📦 Installation page for more details.

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⚖️ License (GPL-3.0)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software") to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Variational Inference for Multiply-Reported social network data https://doi.org/10.1093/jrsssa/qnac004

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