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Harmonize "See also" crossrefs

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François Briatte 2019-04-21 09:55:14 +02:00
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@ -648,7 +648,7 @@ Inspired by [Awesome Deep Learning](https://github.com/ChristosChristofidis/awes
- [GERGM](https://CRAN.R-project.org/package=GERGM) - Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM). - [GERGM](https://CRAN.R-project.org/package=GERGM) - Estimation and diagnosis of the convergence of Generalized Exponential Random Graph Models (GERGM).
- [geomnet](https://CRAN.R-project.org/package=geomnet) - Single-geometry approach to network visualization with ggplot2. - [geomnet](https://CRAN.R-project.org/package=geomnet) - Single-geometry approach to network visualization with ggplot2.
- [ggnetwork](https://CRAN.R-project.org/package=ggnetwork) - Multiple-geometries approach to plot network objects with ggplot2. - [ggnetwork](https://CRAN.R-project.org/package=ggnetwork) - Multiple-geometries approach to plot network objects with ggplot2.
- [ggraph](https://CRAN.R-project.org/package=ggraph) - Grammar of graph graphics built in the spirit of ggplot2 (see also `tidygraph` further down). - [ggraph](https://CRAN.R-project.org/package=ggraph) - Grammar of graph graphics built in the spirit of ggplot2. See also: `tidygraph` R package.
- [graphlayouts](https://cran.r-project.org/package=graphlayouts) - Layout algorithms based on the concept of [stress majorization](https://doi.org/10.1007/978-3-540-31843-9_25). - [graphlayouts](https://cran.r-project.org/package=graphlayouts) - Layout algorithms based on the concept of [stress majorization](https://doi.org/10.1007/978-3-540-31843-9_25).
- [Introducing graphlayouts with Game of Thrones](http://blog.schochastics.net/post/introducing-graphlayouts-with-got/) - [Introducing graphlayouts with Game of Thrones](http://blog.schochastics.net/post/introducing-graphlayouts-with-got/)
- [hergm](https://CRAN.R-project.org/package=hergm) - Estimate and simulate hierarchical exponential-family random graph models (HERGM) with local dependence. - [hergm](https://CRAN.R-project.org/package=hergm) - Estimate and simulate hierarchical exponential-family random graph models (HERGM) with local dependence.
@ -690,7 +690,7 @@ Inspired by [Awesome Deep Learning](https://github.com/ChristosChristofidis/awes
- [Guides for Using the statnet Package](http://www.melissaclarkson.com/resources/R_guides/) (2010). - [Guides for Using the statnet Package](http://www.melissaclarkson.com/resources/R_guides/) (2010).
- [Modeling Valued Networks with statnet](https://statnet.org/trac/raw-attachment/wiki/Sunbelt2013/Valued.pdf) (2013). - [Modeling Valued Networks with statnet](https://statnet.org/trac/raw-attachment/wiki/Sunbelt2013/Valued.pdf) (2013).
- [tergm](https://CRAN.R-project.org/package=tergm) - Fit, simulate and diagnose models for temporal exponential-family random graph models (TERGM). - [tergm](https://CRAN.R-project.org/package=tergm) - Fit, simulate and diagnose models for temporal exponential-family random graph models (TERGM).
- [tidygraph](https://CRAN.R-project.org/package=tidygraph) - 'Tidy' approach to building graph structures. - [tidygraph](https://CRAN.R-project.org/package=tidygraph) - 'Tidy' approach to building graph structures. See also: `ggraph` R package.
- [Introducing tidygraph](https://www.data-imaginist.com/2017/introducing-tidygraph/) - [Introducing tidygraph](https://www.data-imaginist.com/2017/introducing-tidygraph/)
- [Tidying up your network analysis with tidygraph and ggraph](https://www.rstudio.com/resources/videos/tidying-up-your-network-analysis-with-tidygraph-and-ggraph/) - [Tidying up your network analysis with tidygraph and ggraph](https://www.rstudio.com/resources/videos/tidying-up-your-network-analysis-with-tidygraph-and-ggraph/)
- [tnam](https://CRAN.R-project.org/package=tnam) - Tools to fit temporal and cross-sectional network autocorrelation models (TNAM). - [tnam](https://CRAN.R-project.org/package=tnam) - Tools to fit temporal and cross-sectional network autocorrelation models (TNAM).