From d62aec14aa4b4a6d20058bf95075d784db3cc343 Mon Sep 17 00:00:00 2001 From: "Pavel N. Krivitsky" Date: Tue, 14 May 2024 19:20:39 +1000 Subject: [PATCH] Created a subsection for dynamic networks and added a number of packages (rem, relevent, dnr, RSiena, lolog). references cran-task-views/ctv#61 --- ctv-NetworkAnalysis.md | 35 +++++++++++++++++++++++++++++------ 1 file changed, 29 insertions(+), 6 deletions(-) diff --git a/ctv-NetworkAnalysis.md b/ctv-NetworkAnalysis.md index 7b72509..d7a7ca6 100644 --- a/ctv-NetworkAnalysis.md +++ b/ctv-NetworkAnalysis.md @@ -100,22 +100,18 @@ Both main packages can compute betweenness, eigenvalue, power, and closeness cen This section does not include Bayesian networks and Markov Random-Field models as they merely represent relations between variables and are listed in the CRAN TaskView on [Graphical Models](https://cran.r-project.org/web/views/GraphicalModels.html) -- `r pkg("goldfish")` offers functions for fitting statistical network models to dynamic network data (both dynamic network actor models and relational event models). - -- `r pkg("ergm")` provides function to fit, simulate and analyse exponential random graph models (ERGM). Depending on specific needs, several specialised extentions are available (for an alternative see `r pkg("dnr")`): +- `r pkg("ergm")` provides function to fit, simulate and analyse exponential-family random graph models (ERGM). Depending on specific needs, several specialised extentions are available |Use case|Package| |--------|-------| |Count weights|`r pkg("ergm.count")`| |Egocentrically sampled networks|`r pkg("ergm.ego")`| -|Multilayer networks|`r pkg("ergm.multi")`| +|Multilayer networks and samples of networks|`r pkg("ergm.multi")`| |Rank-order networks|`r pkg("ergm.rank")`| |Modeling ERGM-generating processes|`r pkg("ergmgp")`| |Fit ERGM to small networks|`r pkg("ergmgp")`| |Small hierarchical ERGMs|`r pkg("lightergm")`| |Large hierarchical ERGMs|`r pkg("biergm")`| -|Analyse and simulate temporal networks|`r pkg("tergm")`| -|Bootstrap assessment of (temporal) ERGM|`r pkg("btergm")`| - `r pkg("amen")` offer additive and multiplicative effect (AME) models with regression terms, covariance structure of the social relations model (Warner, Kenny and Stoto ([1979](https://doi.org/10.1037/0022-3514.37.10.1742)), and multiplicative factor models (Hoff [2009](https://doi.org/10.1007/s10588-008-9040-4)). It supports binary networks as well as valued ones (assuming a Gaussian, zero-inflated/tobit, ordinal, or fixed-rank nomination model) @@ -130,6 +126,33 @@ This section does not include Bayesian networks and Markov Random-Field models a - `r pkg("nda")` gathers non-parametric dimensionality-reduction functions with/out (automated) feature selection and limited plotting capabilities. +- `r pkg("lolog")` implements Latent Order Logistic (LOLOG) models, a network formation process in which edges are added one at a time drawn from a distribution conditional on edges already added, with order unknown. + +## Dynamic Networks + +The following packages focus on modeling and simulation of networks that evolve over time and network processes that occur over time. + +### Relational Events + +Relational event data contains information about exact times during which the nodes interact. This is commonly observed for e-mail, radio, and other communications. + +- `r pkg("rem")` and `r pkg("relevent")` both contain functions to fit and simulate dyad-oriented relational event models. `r pkg("relevent")` can also estimate event sequence data without time stamps. + +- `r pkg("goldfish")` offers functions to fit and simulate actor-oriented dynamic network actor models and dyad-oriented relational event models. + +### Discrete Observations + +The following packages are focused on modeling series of networks, also known as panel data. + +- `r pkg("tergm")` a set of extensions for `r pkg("ergm")` for fitting and simulating discrete-time models for series of networks (or a long-term equilibrium of a discrete-time network process) where each time step is modeled as a draw from an ERGM conditional on the prior time steps. + +- `r pkg("dnr")` estimation of discrete-time models for series of networks where each time-step is modeled as a draw from an ERGM conditional on prior time steps, subject to the constraint that within each time step, edge variables are independent. Varying node sets are also supported. + +- `r pkg("btergm")` bootstrap inference for discrete-time models for series of networks where each time step is modeled as a draw from an ERGM conditional on the prior time steps. + +- `r pkg("RSiena")` estimation of continuous-time Stochastic Actor-Oriented Models (SAOMs) for panel network data. + + # Ad-hoc Packages Being a flexible method, network analysis is used in a number of fields with specific needs addressed by ad-hoc packages.