Package: paramix 0.0.3

Carl Pearson

paramix: Aggregate and Disaggregate Continuous Parameters for Compartmental Models

A convenient framework for aggregating and disaggregating continuously varying parameters (for example, case fatality ratio, with age) for proper parametrization of lower-resolution compartmental models (for example, with broad age categories) and subsequent upscaling of model outputs to high resolution (for example, as needed when calculating age-sensitive measures like years-life-lost). Methods and application to calculating years of life lost are described in Goodfellow et al. (2025) <doi:10.1371/journal.pcbi.1013420>.

Authors:Carl Pearson [aut, cre], Simon Proctor [aut], Lucy Goodfellow [aut]

paramix_0.0.3.tar.gz
paramix_0.0.3.zip(r-4.7-any)paramix_0.0.3.zip(r-4.6-any)
paramix_0.0.3.tar.gz(r-4.7-any)paramix_0.0.3.tar.gz(r-4.6-any)
paramix_0.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
paramix/json (API)

# Install 'paramix' in R:
install.packages('paramix', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/accidda/paramix/issues

Pkgdown/docs site:https://accidda.github.io

On CRAN:

Conda:

3.00 score 1 stars 7 scripts 315 downloads 6 exports 1 dependencies

Last updated from:9333f86689. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK131
source / vignettesOK228
linux-releaseOK135
windows-develOK116
windows-releaseOK91
wasm-releaseOK114

Exports:alembicblenddistilldistill_summaryinterpolate_optsparameter_summary

Dependencies:data.table

Introductory Usage: paramix
Motivating Example | Parametrizing a Compartmental Model | Typical Application | Summary
Rendered fromintro.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2024-12-09

Reproducing the Paper Analysis
Overview | Prerequisites | Copying the Pipeline | Running the Analysis | Parallel Execution | Environment Initialization (renv) | Modernized R Package Dependencies and Numerical Verification | Table 1: Benchmark 65+ Infection Fatality Ratios (IFRs) | Table 2: Overestimate of Intervention Benefits under Naive Discretization | Pipeline Architecture | 1. Inputs (input/) | 2. Simulations and Distillation (output/) | 3. Figures and Summary Statistics (figure/) | Cleaning Build Artifacts
Rendered fromreproducing-paper-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-10-29
Started: 2026-10-29