Package: survinger 0.1.1

survinger: Design-Adjusted Inference for Pathogen Lineage Surveillance

Provides tools for optimizing sequencing resource allocation and estimating pathogen lineage prevalence under real-world genomic surveillance conditions. Implements constrained allocation optimization for limited sequencing capacity across multiple regions and sample sources. Includes Horvitz-Thompson and post-stratified estimators that account for unequal sequencing rates, delay-adjusted nowcasting for right-censored reporting data, and combined design-weighted delay-corrected inference with uncertainty propagation.

Authors:Cuiwei Gao [aut, cre, cph]

survinger_0.1.1.tar.gz
survinger_0.1.1.zip(r-4.7)survinger_0.1.1.zip(r-4.6)survinger_0.1.1.zip(r-4.5)
survinger_0.1.1.tgz(r-4.6-any)survinger_0.1.1.tgz(r-4.5-any)
survinger_0.1.1.tar.gz(r-4.7-any)survinger_0.1.1.tar.gz(r-4.6-any)
survinger_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
survinger/json (API)

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

Bug tracker:https://github.com/cuiweig/survinger/issues

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

Datasets:

On CRAN:

Conda:

biostatisticsepidemiologyhorvitz-thompsonpathogen-surveillancesurvey-sampling

5.20 score 1 stars 20 scripts 496 downloads 30 exports 28 dependencies

Last updated from:1a4906d4c2. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK169
source / vignettesOK229
linux-release-x86_64OK206
macos-release-arm64OK158
macos-oldrel-arm64OK170
windows-develOK138
windows-releaseOK123
windows-oldrelOK127
wasm-releaseOK120

Exports:glancesurv_adjusted_prevalencesurv_bindsurv_compare_allocationssurv_compare_estimatessurv_designsurv_design_effectsurv_detection_probabilitysurv_estimatesurv_estimate_delaysurv_filtersurv_lineage_prevalencesurv_naive_prevalencesurv_nowcast_lineagesurv_optimize_allocationsurv_plot_allocationsurv_plot_sequencing_ratessurv_power_curvesurv_prevalence_bysurv_qualitysurv_reportsurv_reporting_probabilitysurv_required_sequencessurv_sensitivitysurv_set_weightssurv_simulatesurv_tablesurv_update_ratestheme_survingertidy

Dependencies:backportscheckmateclicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigpurrrR6RColorBrewerrlangS7scalestibbletidyselectutf8vctrsviridisLitewithr

Real-World Case Study: European COVID-19 Genomic Surveillance
Motivation | Data source | Setting up the design | Sequencing inequality | The bias problem: weighted vs naive | Optimal resource allocation | Delay correction and nowcasting | Combined correction | Key takeaways | Reproducibility

Last update: 2026-03-27
Started: 2026-03-27

Delay-Adjusted Nowcasting
The right-truncation problem | Estimating the delay distribution | Reporting probability | Nowcasting | Combined design + delay correction | Choosing a delay distribution

Last update: 2026-03-27
Started: 2026-03-27

Optimizing Sequencing Resource Allocation
Why allocation matters | The three objectives | Example | Optimize for minimum MSE | Compare all strategies | With minimum coverage constraints | Choosing an objective

Last update: 2026-03-27
Started: 2026-03-27

Introduction to survinger
The problem | What survinger does | Quick start | Comparing weighted vs naive estimates | Optimizing resource allocation | Delay correction | Combined correction | Detection power | How survinger differs from phylosamp | Next steps

Last update: 2026-03-27
Started: 2026-03-27

Readme and manuals

Help Manual

Help pageTopics
One-row summary of survinger modelglance.surv glance.surv_adjusted glance.surv_delay_fit glance.surv_prevalence
Plot methods for survinger objectsplot.surv plot.surv_adjusted plot.surv_allocation plot.surv_delay_fit plot.surv_design plot.surv_nowcast plot.surv_prevalence
Combined design-weighted and delay-adjusted prevalenceas.data.frame.surv_adjusted print.surv_adjusted surv_adjusted_prevalence
Optimize sequencing allocation across strataas.data.frame.surv_allocation print.surv_allocation surv_optimize_allocation
Estimate reporting delay distributionprint.surv_delay_fit surv_estimate_delay
Create a genomic surveillance design objectprint.summary.surv_design print.surv_design summary.surv_design surv_design
Nowcast lineage counts correcting for reporting delaysas.data.frame.surv_nowcast print.surv_nowcast surv_nowcast_lineage
Estimate lineage prevalence with design weightsas.data.frame.surv_prevalence print.surv_prevalence surv_lineage_prevalence
Example SARS-CoV-2 genomic surveillance datasarscov2_surveillance
Combine multiple prevalence estimatessurv_bind
Compare multiple allocation strategiessurv_compare_allocations
Compare weighted vs naive prevalence estimatessurv_compare_estimates
Compute design effect over timesurv_design_effect
Variant detection probability under current designsurv_detection_probability
Pipe-friendly surveillance analysissurv_estimate
Subset a surveillance design by filter criteriasurv_filter
Compute naive (unweighted) lineage prevalencesurv_naive_prevalence
Plot allocation plansurv_plot_allocation
Plot sequencing rate inequality across stratasurv_plot_sequencing_rates
Compute power curve for detection across prevalence rangeplot.surv_power_curve surv_power_curve
Estimate prevalence by subgroupsurv_prevalence_by
Compute surveillance quality metricssurv_quality
Generate a comprehensive surveillance system reportsurv_report
Compute cumulative reporting probabilitysurv_reporting_probability
Required sequences for target detection probabilitysurv_required_sequences
Sensitivity analysis across methodssurv_sensitivity
Override design weights with custom valuessurv_set_weights
Simulate genomic surveillance datasurv_simulate
Format prevalence results for knitr tablessurv_table
Update sequencing rates in a surveillance designsurv_update_rates
Publication-quality ggplot2 themetheme_survinger
Extract tidy estimates from survinger objectstidy.surv tidy.surv_adjusted tidy.surv_allocation tidy.surv_delay_fit tidy.surv_nowcast tidy.surv_prevalence