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lfq_fit(), lfq_advantage(), lfq_forecast(),
lfq_score() enable tidyverse-style chaining with |>.register_engine() / unregister_engine() allow
third-party packages to register custom modeling engines, similar to
the parsnip engine system.lfq_summary(): One-row-per-lineage overview combining growth rates,
confidence intervals, and relative Rt in a single tibble.as.data.frame.lfq_data(): Clean tibble export for interoperability.fit_model() now accepts both built-in and registered engine names.lfq_engines() lists all available engines including custom registrations.Any scripts or data that you put into this service are public.
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