Design-Adjusted Inference for Pathogen Lineage Surveillance

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Documentation for package ‘survinger’ version 0.1.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

as.data.frame.surv_adjusted	Combined design-weighted and delay-adjusted prevalence
as.data.frame.surv_allocation	Optimize sequencing allocation across strata
as.data.frame.surv_nowcast	Nowcast lineage counts correcting for reporting delays
as.data.frame.surv_prevalence	Estimate lineage prevalence with design weights
glance.surv	One-row summary of survinger model
glance.surv_adjusted	One-row summary of survinger model
glance.surv_delay_fit	One-row summary of survinger model
glance.surv_prevalence	One-row summary of survinger model
plot.surv	Plot methods for survinger objects
plot.surv_adjusted	Plot methods for survinger objects
plot.surv_allocation	Plot methods for survinger objects
plot.surv_delay_fit	Plot methods for survinger objects
plot.surv_design	Plot methods for survinger objects
plot.surv_nowcast	Plot methods for survinger objects
plot.surv_power_curve	Compute power curve for detection across prevalence range
plot.surv_prevalence	Plot methods for survinger objects
print.summary.surv_design	Create a genomic surveillance design object
print.surv_adjusted	Combined design-weighted and delay-adjusted prevalence
print.surv_allocation	Optimize sequencing allocation across strata
print.surv_delay_fit	Estimate reporting delay distribution
print.surv_design	Create a genomic surveillance design object
print.surv_nowcast	Nowcast lineage counts correcting for reporting delays
print.surv_prevalence	Estimate lineage prevalence with design weights
sarscov2_surveillance	Example SARS-CoV-2 genomic surveillance data
summary.surv_design	Create a genomic surveillance design object
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
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
surv_estimate_delay	Estimate reporting delay distribution
surv_filter	Subset a surveillance design by filter criteria
surv_lineage_prevalence	Estimate lineage prevalence with design weights
surv_naive_prevalence	Compute naive (unweighted) lineage prevalence
surv_nowcast_lineage	Nowcast lineage counts correcting for reporting delays
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	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	Extract tidy estimates from survinger objects
tidy.surv_adjusted	Extract tidy estimates from survinger objects
tidy.surv_allocation	Extract tidy estimates from survinger objects
tidy.surv_delay_fit	Extract tidy estimates from survinger objects
tidy.surv_nowcast	Extract tidy estimates from survinger objects
tidy.surv_prevalence	Extract tidy estimates from survinger objects