antibioticR-package: Analysis of Antbiotic Resistance Data
In tpetzoldt/antibioticR: Analysis of Antibiotic Resistance Data

Description Details Author(s) References

The package aims to improve accesibility to statistical methods for analysing populations of resistant and non-resistant bacteria from an environmental, i.e. non-clinical perspective. The methods are intended to describe sensitivity, tolerance and resistance on a sub-acute level in order to compare populations of different origin on gradual scales.

The package supports currently three methods: (1) Kernel density smoothing for getting mean values and multiple modes from the distributions, (2) an implementation of the ECOFFinder algoritthm (Turnidge, 2006) with automatic start value estimation and a shiny app for interactive use, and (3) Maximum likelihood estimation of multi-modal normal and exponential-normal mixtures.

Thomas Petzoldt

Bolker, Ben and R Development Core Team (2017) bbmle: Tools for General Maximum Likelihood Estimation. R package version 1.0.20. https://CRAN.R-project.org/package=bbmle

Gruen, Bettina and Leisch, Friedrich (2008) FlexMix Version 2: Finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28(4), 1-35 doi: 10.18637/jss.v028.i04

The European Committee on Antimicrobial Susceptibility Testing (2018). Breakpoint tables for interpretation of MICs and zone diameters, version 8.0, http://www.eucast.org/clinical_breakpoints/ accessed: 2018-07-09

Turnidge, J., Kahlmeter, G., Kronvall, G. (2006) Statistical characterization of bacterial wild-type MIC value distributions and the determination of epidemiological cut-off values. Clin Microbial Infect 12: 418-425 doi: 10.1111/j.1469-0691.2006.01377.x

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0

tpetzoldt/antibioticR documentation built on Sept. 25, 2021, 1:17 p.m.