Novel methods are needed to analyze the large amounts of antimicrobial resistance (AMR) data generated by AMR surveillance programs. This package is used to estimate resistance relationship networks, or 'Rnets', from empirical antimicrobial susceptibility data. These networks can be used to study relationships between antimicrobial resistances (typically measured using MICs) and genes in populations. The 'GitHub' for this package is available at <https://GitHub.com/EpidemiologyDVM/Rnets>. Bug reports and features requests should be directed to the same 'GitHub' site. \nReferences for the relevant analytic methods are provided below:\nWJ Love, et al., "Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012" (2016)<doi:https://doi.org/10.1371/journal.pcbi.1005160>\nJ Friedman, T Hastie, R Tibshirani "Sparse inverse covariance estimation with the graphical lasso" (2007) <doi:https://doi.org/10.1093/biostatistics/kxm045>\nH Liu, K Roeder, L Wasserman "Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models" (2010) <arXiv:https://arxiv.org/pdf/1006.3316.pdf>\n S Gomez, P Jensen, A Arenas. "Analysis of community structure in networks of correlated data" (2009) <doi: http://doi.org/10.1103/PhysRevE.80.016114>.
|Maintainer||William Love <[email protected]>|
|Package repository||View on GitHub|
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