From 1710e220dd4336b319ee225ebef8c7a7b0b475cd Mon Sep 17 00:00:00 2001 From: Matthijs Berends Date: Mon, 2 Jun 2025 12:11:00 +0200 Subject: [PATCH] AMR v3.0\! --- CRAN-SUBMISSION | 6 +++--- DESCRIPTION | 2 +- NEWS.md | 4 +--- R/antibiogram.R | 4 ++-- R/g.test.R | 2 +- man/antibiogram.Rd | 4 ++-- man/g.test.Rd | 2 +- 7 files changed, 11 insertions(+), 13 deletions(-) diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION index da12eb989..7d94ea663 100644 --- a/CRAN-SUBMISSION +++ b/CRAN-SUBMISSION @@ -1,3 +1,3 @@ -Version: 2.1.1 -Date: 2023-10-20 16:05:16 UTC -SHA: ca72a646d041f7f096c4e196e8ae2fb2b176019c +Version: 3.0.0 +Date: 2025-06-01 16:52:53 UTC +SHA: 79038fed2169a25a7fc067c80bb25d9d78be21d9 diff --git a/DESCRIPTION b/DESCRIPTION index 3c95254c2..59b9b69d5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: AMR -Version: 2.1.1.9290 +Version: 3.0.0 Date: 2025-06-01 Title: Antimicrobial Resistance Data Analysis Description: Functions to simplify and standardise antimicrobial resistance (AMR) diff --git a/NEWS.md b/NEWS.md index d02c257e3..a745477e7 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,4 @@ -# AMR 2.1.1.9290 - -*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://amr-for-r.org/#get-this-package).)* +# AMR 3.0.0 This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island's Atlantic Veterinary College](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change. diff --git a/R/antibiogram.R b/R/antibiogram.R index fcf4c5837..b9df118a5 100755 --- a/R/antibiogram.R +++ b/R/antibiogram.R @@ -110,7 +110,7 @@ #' #' There are various antibiogram types, as summarised by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()]. #' -#' For clinical coverage estimations, **use WISCA whenever possible**, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki *et al.* (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome. +#' For clinical coverage estimations, **use WISCA whenever possible**, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki *et al.* (2020, \doi{10.1001/jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome. #' #' 1. **Traditional Antibiogram** #' @@ -266,7 +266,7 @@ #' For more background, interpretation, and examples, see [the WISCA vignette](https://amr-for-r.org/articles/WISCA.html). #' @source #' * Bielicki JA *et al.* (2016). **Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data** *Journal of Antimicrobial Chemotherapy* 71(3); \doi{10.1093/jac/dkv397} -#' * Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries** *JAMA Netw Open.* 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124} +#' * Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries** *JAMA Netw Open.* 3(2):e1921124; \doi{10.1001/jamanetworkopen.2019.21124} #' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373} #' * Barbieri E *et al.* (2021). **Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2} #' * **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. . diff --git a/R/g.test.R b/R/g.test.R index 983b1bd15..c07886ded 100755 --- a/R/g.test.R +++ b/R/g.test.R @@ -72,7 +72,7 @@ #' #' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use *G*-tests for each category, of course. #' @seealso [chisq.test()] -#' @references 1. McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. . +#' @references 1. McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. #' @source The code for this function is identical to that of [chisq.test()], except that: #' - The calculation of the statistic was changed to \eqn{2 * sum(x * log(x / E))} #' - Yates' continuity correction was removed as it does not apply to a *G*-test diff --git a/man/antibiogram.Rd b/man/antibiogram.Rd index d55e2d74e..7709eeaea 100644 --- a/man/antibiogram.Rd +++ b/man/antibiogram.Rd @@ -11,7 +11,7 @@ \source{ \itemize{ \item Bielicki JA \emph{et al.} (2016). \strong{Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data} \emph{Journal of Antimicrobial Chemotherapy} 71(3); \doi{10.1093/jac/dkv397} -\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124} +\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001/jamanetworkopen.2019.21124} \item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373} \item Barbieri E \emph{et al.} (2021). \strong{Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2} \item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. @@ -163,7 +163,7 @@ Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibi There are various antibiogram types, as summarised by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}. -For clinical coverage estimations, \strong{use WISCA whenever possible}, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki \emph{et al.} (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section \emph{Explaining WISCA} on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome. +For clinical coverage estimations, \strong{use WISCA whenever possible}, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki \emph{et al.} (2020, \doi{10.1001/jamanetworkopen.2019.21124}). See the section \emph{Explaining WISCA} on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome. \enumerate{ \item \strong{Traditional Antibiogram} diff --git a/man/g.test.Rd b/man/g.test.Rd index d24d16e04..39a42bc1f 100644 --- a/man/g.test.Rd +++ b/man/g.test.Rd @@ -133,7 +133,7 @@ g.test(x) } \references{ \enumerate{ -\item McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}. +\item McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. } } \seealso{