Add network analysis packages

NetworkAnalysis.md

@@ -3,7 +3,7 @@ name: NetworkAnalysis
 topic: Network Analysis
 maintainer: Fabio Ashtar Telarico, Pavel N. Krivitsky, James Hollway
 email: Fabio-Ashtar.Telarico@fdv.uni-lj.si
-version: 2025-12-05
+version: 2026-09-02
 source: https://github.com/cran-task-views/NetworkAnalysis/
 ---
 
@@ -188,6 +188,11 @@ allows to easily convert objects produced by Statnet packages into
 leveraging multiple packages' functionalities and ensuring compatibility between
 several users' workflows too many additional functionalities.
 
+- `r pkg("rgexf")` creates, reads, and writes graphs in the GEXF exchange
+format used by Gephi. It supports node and edge attributes, visualization
+attributes, dynamic networks, and edge weights, and interoperates with
+`r pkg("igraph")` objects.
+
 - Similarly, `r pkg("netUtils")` supplies a collection of helper functions for
 working with network objects including the extraction of sub‑graphs, computing
 basic statistics, converting between common network classes (not least
@@ -285,6 +290,10 @@ providing function to easily produce hierarchical clustering
 (`neatmaps::hierarchy`), consensus clustering (`neatmaps::consClustResTable`)
 and heatmaps of multiple networks (`neatmaps::neatmap`).
 
+- `r pkg("netplot")` draws `r pkg("igraph")` and `r pkg("network")` objects
+using the grid graphics system, with aesthetically oriented defaults for
+out-of-the-box network visualizations.
+
 - `r pkg("autograph")` builds on `r pkg("ggraph")` for drawing graphs and plots
 of network objects with sensible defaults and consistent theming in a range of
 institutional styles. 
@@ -534,6 +543,8 @@ several specialized extensions are available.
   | Template for implementing custom network effects (non-CRAN) | `r github("statnet/ergm.userterms")`     |
   | User-contributed network effects (non-CRAN)                 | `r github("statnet/ergm.terms.contrib")` |
 
+- `r pkg("tabulergm")` provides functions to create publication-ready tables of ERGM terms, their mathematical definitions, descriptions, and graphical representations (figures). Tables generated by `tabulergm` can be used in Quarto and R Markdown documents.
+
 - `r pkg("amen")` offers additive and multiplicative effect (AME) models with
 regression terms, covariance structure of the social relations model,
 and multiplicative factor models.
@@ -566,7 +577,6 @@ attributes, relations between individuals, and size-related factors.
 - `r pkg("networkscaleup")` implements methods for estimating, among other things, degree and sizes of hidden populations based on Aggregated Relational Data (ARD) -- number of people known in a set of sub-populations. The methods include "classical" MLE estimators as well as more recent Bayesian models.
 
 
-
 ### Multimodal and multilevel networks
 
 - `r pkg("migraph")` is an `r pkg("igraph")` extension to analyze multimodal
@@ -657,9 +667,14 @@ contagion processes. It implements algorithms for calculating network diffusion
 statistics such as transmission rate, hazard rates, exposure models, network
 threshold levels, infectiousness (contagion), and susceptibility.
 
+- `r pkg("epiworldR")` provides a fast and flexible framework for simulating
+agent-based epidemic models on networks. It includes common compartmental
+models such as SIS, SIR, and SEIR and supports user-defined disease processes,
+interventions, and multiple-disease simulations.
+
 ### Others
 
-- `r pkg("graphon")` provides methods for estimating the *graphon* of a network based on tis adjacency matrix using empirical degree-sorting for stochastic blockmodel (SBM), SBM approximation, universal singular value thresholding, or neighborhood smoothing. Also, on the basis of the estiamted model, it can complete a matrix from a partially observed data. Additionally, it includes function to generate binary graph given an arbitrary graphon, Erdos-Renyi random graphs, and SBMs. Besides including 10 graphon models for simulation.
+- `r pkg("graphon")` provides methods for estimating the *graphon* of a network based on its adjacency matrix using empirical degree-sorting for stochastic block model (SBM), SBM approximation, universal singular value thresholding, or neighborhood smoothing. Also, on the basis of the estimated model, it can complete a matrix from partially observed data. Additionally, it includes functions to generate a binary graph given an arbitrary graphon, Erdos-Renyi random graphs, and SBMs, and includes 10 graphon models for simulation.
 
 ## Field packages