f7e9294bea
* Add parallel computing support to antibiogram() and wisca() (#281) For WISCA: simulations are distributed across (group, chunk) job pairs via future.apply::future_lapply(), keeping all workers active even when the regimen count is smaller than nbrOfWorkers(). Sequential fallback with progress ticker is preserved when parallel = FALSE or workers = 1. For grouped antibiograms: each group is processed by a separate worker, mirroring the row-batch approach in as.sir(). Same gate pattern as as.sir() (PR #280): requires a non-sequential future::plan() to be active; auto-upgrades to parallel = TRUE when a parallel plan is detected; throws an informative error otherwise. https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF * Fix version to 3.0.1.9055 and update CLAUDE.md version formula Uses origin/${defaultbranch} (with a fetch) instead of the local branch ref so the commit count is never stale after a merge. https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF * Fix non-ASCII characters in antibiogram.R Replace en/em dashes and non-breaking spaces with ASCII equivalents to satisfy R CMD check portability requirement. https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF * Update auto-generated Rd files after documentation rebuild https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF * Move parallel gate to top of antibiogram.default() like sir.R The gate was inside the wisca==TRUE block, so parallel=TRUE with a sequential plan was silently ignored for non-WISCA antibiograms. Now the gate runs unconditionally at the top of the function, identical to the as.sir() pattern: error on explicit parallel=TRUE with sequential plan, auto-upgrade when a non-sequential plan is already active. https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF * Fix parallel WISCA returning all NA; strengthen tests; add sequential hint Bug: lapply() over a factor yields length-1 factor elements (integer codes), while for() over a factor yields character strings. The job list stored j\$group as a factor integer, but the reassembly loop compared it with identical(j\$group, g) where g was character -- always FALSE, so no simulation chunks were ever assembled and coverage stayed NA throughout. Fix: convert unique_groups to character before building jobs so both the job list and the reassembly loop use the same type. Tests: replaced na.rm = TRUE guards with explicit anyNA() checks so the test suite would have caught the all-NA result immediately. Also adds a sequential-mode performance hint (analogous to sir.R lines 1116-1127) when simulations >= 500 and >= 3 regimens. https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF --------- Co-authored-by: Claude <noreply@anthropic.com>
The AMR Package for R
Please visit our comprehensive package website https://amr-for-r.org to read more about this package, including many examples and tutorials.
Overview:
- Provides an all-in-one solution for antimicrobial resistance (AMR) data analysis in a One Health approach
- Peer-reviewed, used in over 175 countries, available in 28 languages
- Generates antibiograms - traditional, combined, syndromic, and even WISCA
- Provides the full microbiological taxonomy of ~79 000 distinct species and extensive info of ~620 antimicrobial drugs
- Applies CLSI 2011-2026 and EUCAST 2011-2026 clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation
- Corrects for duplicate isolates, calculates and predicts AMR per antimicrobial class
- Integrates with WHONET, ATC, EARS-Net, PubChem, LOINC, SNOMED CT, and NCBI
- 100% free of costs and dependencies, highly suitable for places with limited resources
The AMR package is a peer-reviewed, free and open-source R package
with zero dependencies to simplify the analysis and prediction of
Antimicrobial Resistance (AMR) and to work with microbial and
antimicrobial data and properties, by using evidence-based methods.
Our aim is to provide a standard for clean and reproducible AMR data
analysis, that can therefore empower epidemiological analyses to
continuously enable surveillance and treatment evaluation in any
setting.
The AMR package supports and can read any data format, including
WHONET data. This package works on Windows, macOS and Linux with all
versions of R since R-3.0 (April 2013). It was designed to work in any
setting, including those with very limited resources. It was created
for both routine data analysis and academic research at the Faculty of
Medical Sciences of the University of Groningen
and the University Medical Center Groningen.
How to get this package
To install the latest ‘release’ version from CRAN:
install.packages("AMR")
To install the latest ‘beta’ version:
install.packages("AMR", repos = "beta.amr-for-r.org")
If this does not work, try to install directly from GitHub using the
remotes package:
remotes::install_github("msberends/AMR")
This AMR package for R is free, open-source software and licensed under the GNU General Public License v2.0 (GPL-2). These requirements are consequently legally binding: modifications must be released under the same license when distributing the package, changes made to the code must be documented, source code must be made available when the package is distributed, and a copy of the license and copyright notice must be included with the package.