Biological Structure Analysis

Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information.

Microbial Community Ecology Data Analysis

A series of data analysis approaches for microbiome data based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis.

Null Model Analysis for Ecological Networks

Null models to analyse ecological networks (e.g. food webs, flower-visitation networks, seed-dispersal networks) and detect resource preferences or non-random interactions among network nodes. Tools are provided to run null models, test for and plot preferences, plot and analyse bipartite networks, and export null model results in a form compatible with other network analysis packages. The underlying null model was developed by Agusti et al. (2003) Molecular Ecology and the full application to ecological networks by Vaughan et al. (2018) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology & Evolution, .

Algebraic Tools for the Analysis of Multiple Social Networks

Algebraic procedures for analyses of multiple social networks are provided with this package as described in Ostoic (2020) . 'multiplex' supports the creation and analysis of multiplex, multimode, and multilevel network data in various formats. It combines methods based on partially ordered semigroups, decomposition procedures, semiring structures, and relational bundles for multivariate network analysis. It also provides an algebraic approach to affiliation networks using Galois derivations, with visualization options.

Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks

A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) and Krivitsky, Coletti, and Hens (2023) .

Integrating Data Exchange and Analysis for Networks ('ideanet')

A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.

Spatial Analysis on Network

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) ; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

Analysis of the International Trade Network

Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.

Rank in Network Meta-Analysis

A supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.

Network-Valued Data Analysis

A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" ; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" .