Package index • survinger

Design construction

Create and modify surveillance designs with inverse-probability weights.

print(<surv_design>) summary(<surv_design>) print(<summary.surv_design>) surv_design(): Create a genomic surveillance design object
surv_update_rates(): Update sequencing rates in a surveillance design
surv_set_weights(): Override design weights with custom values
surv_filter(): Subset a surveillance design by filter criteria

Prevalence estimation

Weighted and unweighted lineage prevalence estimators.

print(<surv_prevalence>) as.data.frame(<surv_prevalence>) surv_lineage_prevalence(): Estimate lineage prevalence with design weights
surv_naive_prevalence(): Compute naive (unweighted) lineage prevalence
surv_prevalence_by(): Estimate prevalence by subgroup
surv_estimate(): Pipe-friendly surveillance analysis

Delay correction & nowcasting

Right-truncation-corrected delay fitting and delay-adjusted nowcasts.

print(<surv_delay_fit>) surv_estimate_delay(): Estimate reporting delay distribution
surv_reporting_probability(): Compute cumulative reporting probability
print(<surv_nowcast>) as.data.frame(<surv_nowcast>) surv_nowcast_lineage(): Nowcast lineage counts correcting for reporting delays
print(<surv_adjusted>) as.data.frame(<surv_adjusted>) surv_adjusted_prevalence(): Combined design-weighted and delay-adjusted prevalence

Resource allocation

Optimise sequencing budgets across strata.

print(<surv_allocation>) as.data.frame(<surv_allocation>) surv_optimize_allocation(): Optimize sequencing allocation across strata
surv_compare_allocations(): Compare multiple allocation strategies
surv_required_sequences(): Required sequences for target detection probability

Diagnostics & reporting

Power analysis, design effects, sensitivity, and one-page reports.

surv_detection_probability(): Variant detection probability under current design
surv_power_curve() plot(<surv_power_curve>): Compute power curve for detection across prevalence range
surv_design_effect(): Compute design effect over time
surv_sensitivity(): Sensitivity analysis across methods
surv_report(): Generate a comprehensive surveillance system report
surv_quality(): Compute surveillance quality metrics

Visualisation

Plot methods and comparison helpers.

surv_compare_estimates(): Compare weighted vs naive prevalence estimates
surv_plot_sequencing_rates(): Plot sequencing rate inequality across strata
surv_plot_allocation(): Plot allocation plan
theme_survinger(): Publication-quality ggplot2 theme
plot(<surv_design>) plot(<surv_allocation>) plot(<surv_prevalence>) plot(<surv_delay_fit>) plot(<surv_nowcast>) plot(<surv_adjusted>): Plot methods for survinger objects
surv_power_curve() plot(<surv_power_curve>): Compute power curve for detection across prevalence range

Tidyverse integration

Broom-style tidying and table helpers.

tidy(<surv_prevalence>) tidy(<surv_nowcast>) tidy(<surv_adjusted>) tidy(<surv_allocation>) tidy(<surv_delay_fit>): Extract tidy estimates from survinger objects
glance(<surv_prevalence>) glance(<surv_delay_fit>) glance(<surv_adjusted>): One-row summary of survinger model
surv_bind(): Combine multiple prevalence estimates
surv_table(): Format prevalence results for knitr tables

Simulation

Generate synthetic surveillance datasets for testing and benchmarking.

surv_simulate(): Simulate genomic surveillance data

Package

Package-level documentation.

survinger survinger-package: survinger: Design-Adjusted Inference for Pathogen Lineage Surveillance

Other

glance(<surv_prevalence>) glance(<surv_delay_fit>) glance(<surv_adjusted>): One-row summary of survinger model
plot(<surv_design>) plot(<surv_allocation>) plot(<surv_prevalence>) plot(<surv_delay_fit>) plot(<surv_nowcast>) plot(<surv_adjusted>): Plot methods for survinger objects
sarscov2_surveillance: Example SARS-CoV-2 genomic surveillance data
print(<surv_adjusted>) as.data.frame(<surv_adjusted>) surv_adjusted_prevalence(): Combined design-weighted and delay-adjusted prevalence
surv_bind(): Combine multiple prevalence estimates
surv_compare_allocations(): Compare multiple allocation strategies
surv_compare_estimates(): Compare weighted vs naive prevalence estimates
print(<surv_design>) summary(<surv_design>) print(<summary.surv_design>) surv_design(): Create a genomic surveillance design object
surv_design_effect(): Compute design effect over time
surv_detection_probability(): Variant detection probability under current design
surv_estimate(): Pipe-friendly surveillance analysis
print(<surv_delay_fit>) surv_estimate_delay(): Estimate reporting delay distribution
surv_filter(): Subset a surveillance design by filter criteria
print(<surv_prevalence>) as.data.frame(<surv_prevalence>) surv_lineage_prevalence(): Estimate lineage prevalence with design weights
surv_naive_prevalence(): Compute naive (unweighted) lineage prevalence
print(<surv_nowcast>) as.data.frame(<surv_nowcast>) surv_nowcast_lineage(): Nowcast lineage counts correcting for reporting delays
print(<surv_allocation>) as.data.frame(<surv_allocation>) surv_optimize_allocation(): Optimize sequencing allocation across strata
surv_plot_allocation(): Plot allocation plan
surv_plot_sequencing_rates(): Plot sequencing rate inequality across strata
surv_power_curve() plot(<surv_power_curve>): Compute power curve for detection across prevalence range
surv_prevalence_by(): Estimate prevalence by subgroup
surv_quality(): Compute surveillance quality metrics
surv_report(): Generate a comprehensive surveillance system report
surv_reporting_probability(): Compute cumulative reporting probability
surv_required_sequences(): Required sequences for target detection probability
surv_sensitivity(): Sensitivity analysis across methods
surv_set_weights(): Override design weights with custom values
surv_simulate(): Simulate genomic surveillance data
surv_table(): Format prevalence results for knitr tables
surv_update_rates(): Update sequencing rates in a surveillance design
theme_survinger(): Publication-quality ggplot2 theme
tidy(<surv_prevalence>) tidy(<surv_nowcast>) tidy(<surv_adjusted>) tidy(<surv_allocation>) tidy(<surv_delay_fit>): Extract tidy estimates from survinger objects

Other

glance(<surv_prevalence>) glance(<surv_delay_fit>) glance(<surv_adjusted>): One-row summary of survinger model
plot(<surv_design>) plot(<surv_allocation>) plot(<surv_prevalence>) plot(<surv_delay_fit>) plot(<surv_nowcast>) plot(<surv_adjusted>): Plot methods for survinger objects
sarscov2_surveillance: Example SARS-CoV-2 genomic surveillance data
print(<surv_adjusted>) as.data.frame(<surv_adjusted>) surv_adjusted_prevalence(): Combined design-weighted and delay-adjusted prevalence
surv_bind(): Combine multiple prevalence estimates
surv_compare_allocations(): Compare multiple allocation strategies
surv_compare_estimates(): Compare weighted vs naive prevalence estimates
print(<surv_design>) summary(<surv_design>) print(<summary.surv_design>) surv_design(): Create a genomic surveillance design object
surv_design_effect(): Compute design effect over time
surv_detection_probability(): Variant detection probability under current design
surv_estimate(): Pipe-friendly surveillance analysis
print(<surv_delay_fit>) surv_estimate_delay(): Estimate reporting delay distribution
surv_filter(): Subset a surveillance design by filter criteria
print(<surv_prevalence>) as.data.frame(<surv_prevalence>) surv_lineage_prevalence(): Estimate lineage prevalence with design weights
surv_naive_prevalence(): Compute naive (unweighted) lineage prevalence
print(<surv_nowcast>) as.data.frame(<surv_nowcast>) surv_nowcast_lineage(): Nowcast lineage counts correcting for reporting delays
print(<surv_allocation>) as.data.frame(<surv_allocation>) surv_optimize_allocation(): Optimize sequencing allocation across strata
surv_plot_allocation(): Plot allocation plan
surv_plot_sequencing_rates(): Plot sequencing rate inequality across strata
surv_power_curve() plot(<surv_power_curve>): Compute power curve for detection across prevalence range
surv_prevalence_by(): Estimate prevalence by subgroup
surv_quality(): Compute surveillance quality metrics
surv_report(): Generate a comprehensive surveillance system report
surv_reporting_probability(): Compute cumulative reporting probability
surv_required_sequences(): Required sequences for target detection probability
surv_sensitivity(): Sensitivity analysis across methods
surv_set_weights(): Override design weights with custom values
surv_simulate(): Simulate genomic surveillance data
surv_table(): Format prevalence results for knitr tables
surv_update_rates(): Update sequencing rates in a surveillance design
theme_survinger(): Publication-quality ggplot2 theme
tidy(<surv_prevalence>) tidy(<surv_nowcast>) tidy(<surv_adjusted>) tidy(<surv_allocation>) tidy(<surv_delay_fit>): Extract tidy estimates from survinger objects