|sorenh at math.aau.dk
|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.
|Soren Hojsgaard (2023). CRAN Task View: Graphical Models. Version 2023-04-05. URL https://CRAN.R-project.org/view=GraphicalModels.
|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.
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.
Notice that Structural Equation Models (SEM) packages are in a sense also graphical models. However, SEM packages are not presented here but are they have their own section in the Psychometrics task view.
The packages can be roughly structured into the following topics (although several of them have functionalities which go across these categories):
abn (archived): Modelling Multivariate Data with Additive Bayesian Networks. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. ‘abn’ provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data.
SEMID: Identifiability of Linear Structural Equation Models. Provides routines to check identifiability or non-identifiability of linear structural equation models as described in Drton, Foygel, and Sullivant (2011) doi:10.1214/10-AOS859, Foygel, Draisma, and Drton (2012) doi:10.1214/12-AOS1012, and other works. The routines are based on the graphical representation of structural equation models.
BDgraph: Bayesian Graph Selection Based on Birth-Death MCMC Approach. Bayesian inference for structure learning in undirected graphical models. The main target is to uncover complicated patterns in multivariate data wherein either continuous or discrete variables.
deal: Learning Bayesian Networks with Mixed Variables Bayesian networks with continuous and/or discrete variables can be learned and compared from data.
FBFsearch: Algorithm for searching the space of Gaussian directed acyclic graphical models through moment fractional Bayes factors
GeneNet: Modeling and Inferring Gene Networks. GeneNet is a package for analyzing gene expression (time series) data with focus on the inference of gene networks.
gRc: Inference in Graphical Gaussian Models with Edge and Vertex Symmetries Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours).
gRim: Graphical Interaction Models. Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models.
lvnet: Latent Variable Network Modeling. Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM).
MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks.
networkDynamic: Dynamic Extensions for Network Objects. Simple interface routines to facilitate the handling of network objects with complex intertemporal data. “networkDynamic” is a part of the “statnet” suite of packages for network analysis.
ndtv: Network Dynamic Temporal Visualizations. Renders dynamic network data from ‘networkDynamic’ objects as movies, interactive animations, or other representations of changing relational structures and attributes.
spectralGraphTopology: The package provides estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverages spectral properties of the graphical models as a prior information, which turn out to play key roles in unsupervised machine learning tasks such as community detection.
bnlearn: Bayesian network structure learning via constraint-based (also known as ‘conditional independence’) and score-based algorithms. This package implements the Grow-Shrink (GS) algorithm, the Incremental Association (IAMB) algorithm, the Interleaved-IAMB (Inter-IAMB) algorithm, the Fast-IAMB (Fast-IAMB) algorithm, the Max-Min Parents and Children (MMPC) algorithm and the Hill-Climbing (HC) greedy search algorithm for both discrete and Gaussian networks, along with many score functions and conditional independence tests. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing) are also included.
RHugin: The Hugin Decision Engine (HDE) is commercial software produced by HUGIN EXPERT A/S for building and making inference from Bayesian belief networks. The RHugin package provides a suite of functions allowing the HDE to be controlled from within the R environment for statistical computing. The RHugin package can thus be used to build Bayesian belief networks, enter and propagate evidence, and to retrieve beliefs. Additionally, the RHugin package can read and write hkb and NET files, making it easy to work simultaneously with both the RHugin package and the Hugin GUI. A licensed copy of the HDE (or the trial version) is required for the RHugin package to function, hence the target audience for the package is Hugin users who would like to take advantage of the statistical and programmatic capabilities of R. Notice: RHugin is NOT on CRAN. Link: http://rhugin.r-forge.r-project.org/
pchc Bayesian Network Learning with the PCHC and Related Algorithms. Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then applies the Hill-Climbing greedy search.
|backbone, bayesmix, BDgraph, bnlearn, bnstruct, boa, BRugs, coda, dclone, deal, diagram, DiagrammeR, ergm, FBFsearch, GeneNet, ggm, gRain, gRc, gRim, huge, igraph, lvnet, mgm, MXM, ndtv, network, networkDynamic, pcalg, pchc, qgraph, R2OpenBUGS, R2WinBUGS, rjags, SEMID, sna, spectralGraphTopology.