Efficient Network Enrichment Analysis Test

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

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.

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) .

Generalized Path Analysis for Social Networks

The social network literature features numerous methods for assigning value to paths as a function of their ties. 'gretel' systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) < https://davidbuch.github.io/analyzing-networks-with-gretel.html>. More traditional methods are also implemented, as described in Yang, Knoke (2001) .

Identification and Analysis of Co-Occurrence Networks

Implementation of the NetCutter algorithm described in Müller and Mancuso (2008) . The package identifies co-occurring terms in a list of containers. For example, it may be used to detect genes that co-occur across genomes.