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.
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