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Bayesian Meta-Analysis and Network Meta-Analysis
Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015)
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)
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.
Motif Analysis in Multi-Level Networks
Tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. 'motifr' can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent 'SESMotifAnalyser' 'Python' package written by Tim Seppelt.
Bayesian Package for Network Changepoint Analysis
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
Analysis of Diffusion and Contagion Processes on Networks
Empirical statistical analysis, visualization and simulation of
diffusion and contagion processes on networks. The package implements algorithms
for calculating network diffusion statistics such as transmission rate, hazard
rates, exposure models, network threshold levels, infectiousness (contagion),
and susceptibility. The package is inspired by work published in Valente,
et al., (2015)
Analysis and Mining of Multilayer Social Networks
Functions for the creation/generation and analysis of multilayer social networks
Detecting Outliers in Network Meta-Analysis
A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis : - simple outlier and influential detection measures - outlier and influential detection measures by considering study deletion (shift the mean) - plots for outlier and influential detection measures - Q-Q plot for network meta-analysis - Forward Search algorithm in network meta-analysis. - forward plots to monitor statistics in each step of the forward search algorithm - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.
Statistical Entropy Analysis of Network Data
Statistical entropy analysis of network data as introduced by Frank and Shafie (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)