Training of Neural Networks

Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.

D3 JavaScript Network Graphs from R

Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'.

Fit, Simulate and Diagnose Exponential-Family Models for Networks

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) and Krivitsky, Hunter, Morris, and Klumb (2023) .

Bayesian Network Structure Learning, Parameter Learning and Inference

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing, Tabu Search, DirectLiNGAM) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian, conditional Gaussian and zero-inflated networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, interventions, counterfactuals, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from < https://www.bnlearn.com/>.

Coercion Routines for Network Data Objects

Functions implemented in this package allow to coerce (i.e. convert) network data between classes provided by other R packages. Currently supported classes are those defined in packages: network and igraph.

Weighted Correlation Network Analysis

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Network Meta-Analysis using Frequentist Methods

A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) and supporting Schwarzer et al. (2015) , Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) ; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) ; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) , or penalised logistic regression (Evrenoglou et al., 2022) ; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) ; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) ; - split direct and indirect evidence to check consistency (Dias et al., 2010) , (Efthimiou et al., 2019) ; - scatter plot to visualize local inconsistency (Wilson et al., 2026) ; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) ; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) ; - measures characterizing the flow of evidence between two treatments by König et al. (2013) ; - automated drawing of network graphs described in Rücker & Schwarzer (2016) ; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) ; (Rücker & Schwarzer, 2017) ; - contribution matrix as described in Papakonstantinou et al. (2018) and Davies et al. (2022) ; - network meta-regression with a single continuous or binary covariate (Kwarteng et al., 2026) ; - subgroup network meta-analysis.

Functions for Visualising Simple Graphs (Networks), Plotting Flow Diagrams

Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualising webs, electrical networks, etc. Support for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009), Springer. and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer. Includes demo(flowchart), demo(plotmat), demo(plotweb).

Tidy Geospatial Networks

Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'.

Dynamic Extensions for Network Objects

Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.