Maintainer: | Soren Hojsgaard |
Contact: | sorenh at math.aau.dk |
Version: | 2021-12-27 |
URL: | https://CRAN.R-project.org/view=GraphicalModels |
Source: | https://github.com/cran-task-views/GraphicalModels/ |
Contributions: | Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide. |
Citation: | Soren Hojsgaard (2021). CRAN Task View: Graphical Models. Version 2021-12-27. URL https://CRAN.R-project.org/view=GraphicalModels. |
Installation: | The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("GraphicalModels", coreOnly = TRUE) installs all the core packages or ctv::update.views("GraphicalModels") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details. |
Wikipedia says:
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics — particularly Bayesian statistics — and machine learning.
A supplementary view is that graphical models are based on exploiting conditional independencies for constructing complex stochastic models with a modular structure. That is, a complex stochastic model is built up by simpler building blocks.
This task view is a collection of packages intended to supply R code to deal with graphical models.
The packages can be roughly structured into the following topics (although several of them have functionalities which go across these categories):
diagram Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualising webs, electrical networks, ...
DiagrammeR Build graph/network structures using functions for stepwise addition and deletion of nodes and edges.
graph A package that implements some simple graph handling capabilities.
gRbase The gRbase package provides certain general constructs which are used by other graphical modelling packages. This includes 1) the concept of gmData (graphical meta data), 2) several graph algorithms 3) facilities for table operations, 4) functions for testing for conditional independence. gRbase also illustrates how hierarchical log-linear models (hllm) may be implemented.
igraph Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
network Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
qgraph Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) doi:10.18637/jss.v048.i04
Rgraphviz Provides plotting capabilities for R graph objects.
RBGL A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. (based on graph objects from the graph package).
Core: | gRbase. |
Regular: | bayesmix, BDgraph, bnclassify, bnlearn, bnstruct, boa, BRugs, coda, dclone, diagram, DiagrammeR, ergm, FBFsearch, GeneNet, ggm, gRain, huge, igraph, lvnet, mgm, network, networkDynamic, pcalg, qgraph, R2OpenBUGS, R2WinBUGS, rjags, SIN, sna, spectralGraphTopology. |
Archived: | sparsebn. |