Efficient Network Enrichment Analysis Test

Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, ).

Multiplex Network Differential Analysis (MNDA)

Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) .

Patient-Centered Network Meta-Analysis

Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 ; Lin et al., 2017 ).

Bayesian Analysis of the Network Autocorrelation Model

The network autocorrelation model (NAM) can be used for studying the degree of social influence regarding an outcome variable based on one or more known networks. The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) and Dittrich, Leenders, and Mulder (2019) are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input.

Classes for Relational Data

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.

Network-Based Gene Set Analysis

Carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) , Shojaie A, Michailidis G (2009) , and Ma J, Shojaie A, Michailidis G (2016) .

An Implementation of Sensitivity Analysis in Bayesian Networks

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) .

Differential Network Local Consistency Analysis

Using Local Moran's I for detection of differential network local consistency.

Network Meta-Analysis Database API

Set of functions for accessing database of network meta-analyses described in Petropoulou M, et al. Bibliographic study showed improving statistical methodology of network meta-analyses published between 1999 and 2015 . The database is hosted in a REDcap database at the Institute of Social and Preventive Medicine (ISPM) in the University of Bern.

Micro-Macro Analysis for Social Networks

Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: < https://github.com/sduxbury/netmediate>. BugReports: < https://github.com/sduxbury/netmediate/issues>. Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) . Snijders, Tom A. B., and Christian E. G. Steglich (2015) . Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) . Duxbury, Scott (2023) . Duxbury, Scott (2024) .