Package: exmort 0.1.1

exmort: All-Cause and Excess Mortality Calculator

An interactive 'shiny' application that estimates all-cause mortality and excess mortality from country-level weekly or monthly death counts. Users supply observed deaths and an event calendar (for example COVID-19 waves or typhoons); the app fits one or more statistical baseline models (historical average, negative binomial regression, quasi-Poisson regression, zero-inflated Poisson regression, ARIMA (autoregressive integrated moving average) and SARIMA (seasonal ARIMA) models, GAM (generalized additive model) splines, and the model of Karlinsky and Kobak (2021) <doi:10.7554/eLife.69336>) using periods outside the supplied events, estimates expected deaths across the observed series, and reports excess deaths, P-scores (excess deaths as a percentage of expected deaths) and confidence limits with tables, plots and downloadable reports. Launch the application with run_app().

Authors:Shanlong Ding [aut, cre], World Health Organization Regional Office for the Western Pacific [cph, fnd]

exmort_0.1.1.tar.gz
exmort_0.1.1.zip(r-4.7-any)exmort_0.1.1.zip(r-4.6-any)exmort_0.1.1.zip(r-4.5-any)
exmort_0.1.1.tgz(r-4.6-any)exmort_0.1.1.tgz(r-4.5-any)
exmort_0.1.1.tar.gz(r-4.7-any)exmort_0.1.1.tar.gz(r-4.6-any)
exmort_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
exmort/json (API)

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

Bug tracker:https://github.com/shanlong-who/exmort/issues

Pkgdown/docs site:https://shanlong-who.github.io

On CRAN:

Conda:

4.78 score 418 downloads 1 exports 110 dependencies

Last updated from:7d67c0584e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK228
source / vignettesOK255
linux-releaseOK273
macos-releaseOK194
macos-oldrelOK121
windows-develOK107
windows-releaseOK106
windows-oldrelOK120
wasm-releaseOK173

Exports:run_app

Dependencies:askpassbase64encbslibcachemcellrangerclicolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledigestdplyrDTevaluatefarverfastmapfontawesomeforecastfracdifffsgenericsggplot2ggrepelgluegtablehighrhmshtmltoolshtmlwidgetshttpuvhttrisobandISOweekjquerylibjsonlitekableExtraknitrlabelinglaterlatticelazyevallifecyclelmtestlubridatemagrittrMatrixmemoisemgcvmimenlmennetopensslopenxlsxotelpillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrR6rappdirsRColorBrewerRcppRcppArmadilloreactablereactRreadxlrematchreshape2rlangrmarkdownrstudioapiS7sassscalesshinyshinyalertshinycssloadersshinyjssourcetoolsstringistringrsvglitesyssystemfontstextshapingtibbletidyrtidyselecttimechangetimeDatetinytexurcautf8uuidvctrsviridisLitewithrxfunxml2xtableyamlzipzoo

Choosing models and interpreting excess mortality
Observed, expected and excess deaths | Aggregate the numerator and denominator | Review the baseline first | The model menu | Use the Best Fit tab carefully | Read uncertainty in context | Compare sensitivity and explain the finding | Save a reproducible record
Rendered frommodels-and-interpretation.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-10-06

Getting started with exmort
Install and launch | A first analysis with built-in data | 1. Load an example | 2. Check what was loaded | 3. Run a small model comparison | 4. Read the results | 5. Save the analysis | Prepare your own workbook | Report requirements | Troubleshooting
Rendered fromexmort.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-10-06

Preparing mortality data and event calendars
Start from a supplied template | Preserve the mortality sheet layout | Understand the processed table | Edit the event calendar | Decide what the baseline contains | Upload, merge and verify | Check a downloaded processed table in R
Rendered fromdata-preparation.Rmdusingknitr::rmarkdown

Last update: 2026-10-06
Started: 2026-10-06