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. The methods used in 'Rnets' are available in the following publications: An overview of the method in WJ Love, et al., "Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012" (2016) <doi:10.1371/journal.pcbi.1005160>; The graphical LASSO for sparsity in J Friedman, T Hastie, R Tibshirani "Sparse inverse covariance estimation with the graphical lasso" (2007) <doi:10.1093/biostatistics/kxm045>; L1 penalty selection in H Liu, K Roeder, L Wasserman "Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models" (2010) <arXiv:1006.3316>; Modularity for graphs with negative edge weights in S Gomez, P Jensen, A Arenas. "Analysis of community structure in networks of correlated data" (2009) <doi:10.1103/PhysRevE.80.016114>.
|Author||William Love [aut, cre]|
|Maintainer||William Love <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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