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) .

Data Analysis for IP Addresses and Networks

Classes and functions for working with IP (Internet Protocol) addresses and networks, inspired by the Python 'ipaddress' module. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces. It is specifically designed to work well with the 'tidyverse'.

A 'shiny' Application for Network Meta-Analysis

Conduct network meta-analyses through a graphical user interface using 'bnma', 'gemtc' and 'netmeta' with additional analysis provided by 'meta' and 'metafor'. Frequentist, Bayesian, meta-regression and baseline risk meta-regression analyses can all be conducted using a consistent data structure and terminology. Many options are provided for downloading publication-ready outputs and analyses can be reproduced outside of the application by downloading a 'quarto' file. The interface was generated using 'shinyscholar'. The initial version of the app was described by Owen et al. (2018) , Bayesian ranking visualisations were described by Nevill et al. (2023) and metaregression was described by Morris et al. (2025) .

Analysis of Stream Network Topology and Order

Provides tools for analyzing stream networks, including graph construction, calculation of Link Magnitude, D-LINK, and export of spatial data. The Link Magnitude (Shreve stream order) method follows Shreve (1966) . The D-LINK metric follows Osborne & Wiley (1992) .

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.

Network Meta-Analysis of Multiple Diagnostic Tests

Provides statistical methods for network meta-analysis of diagnostic tests to simultaneously compare multiple tests within a missing data framework, including: - Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests (Ma, Lian, Chu, Ibrahim, and Chen (2018) ) - Bayesian Hierarchical Summary Receiver Operating Characteristic Model for Network Meta-Analysis of Diagnostic Tests (Lian, Hodges, and Chu (2019) ).