Package index • tters

TrialEmulation backend

Drop-in te_datastore backend — expand in Rust, estimate in R, bit-identically to the default path.

expand_trials_tters(): Expand a sequence of target trials with the Rust + Polars engine
save_to_tters(): Create a te_datastore_tters storage backend

Trial expansion (Parquet path)

Read a person-time Parquet file, write the expanded trial frame.

expand_trial(): Expand a target-trial person-time dataset (ergonomic wrapper)
expand_trial_weighted(): Expand a dataset and attach pre-computed inverse-probability weights
expand_trial_weighted_fitted(): Fit IPW weights and expand a cohort into a weighted trial frame (ergonomic wrapper)
fit_trial_weights(): Fit inverse-probability weights for a target-trial cohort (ergonomic wrapper)

Trial expansion (in-memory data.frame)

Frame-in / frame-out, with no intermediate Parquet.

expand_trial_df(): Expand a target-trial cohort data.frame in memory (ergonomic wrapper)
expand_trial_weighted_df(): Expand a cohort data.frame and attach pre-computed weights, in memory (wrapper)
expand_trial_weighted_fitted_df(): Fit IPW weights and expand a cohort data.frame in one call, in memory (wrapper)
fit_trial_weights_df(): Fit inverse-probability weights for a cohort data.frame, in memory (wrapper)

Low-level engine bindings

Thin extendr shims wrapped by the ergonomic functions above.

expand_parquet(): Expand a prepared person-time Parquet dataset into the sequential target-trial layout and write the result to output_path.
expand_weighted_parquet(): Expand a person-time Parquet dataset and attach pre-computed inverse-probability weights, writing the weighted frame to output_path.
expand_weighted_fitted_parquet(): Fit the IPW weights in Rust, expand the cohort, apply the weights, and write the weighted trial frame — a raw cohort to a weighted, expanded frame in one call (no pre-computed factor table).
fit_weights_parquet(): Fit the inverse-probability weight factor for a Parquet cohort in Rust and write the per-(id, period) factor table (id, period, weight_factor).
expand_df(): Expand an in-memory cohort data.frame into the sequential target-trial layout and return the result as a data.frame — the frame-in/frame-out analogue of expand_parquet(), with no intermediate Parquet.
expand_weighted_df(): Expand an in-memory cohort and attach pre-computed inverse-probability weights, returning the weighted frame as a data.frame — the frame-in/frame-out analogue of expand_weighted_parquet().
expand_weighted_fitted_df(): Fit the IPW weights for an in-memory cohort, expand, apply, and return the weighted trial frame as a data.frame — a raw cohort data.frame straight to a weighted, expanded data.frame in one call (no pre-computed factor table, no intermediate Parquet). The frame-in/frame-out analogue of expand_weighted_fitted_parquet(). A 64-bit integer id (bit64::integer64) round-trips exactly.
fit_weights_df(): Fit the inverse-probability weight factor for an in-memory cohort and return the per-(id, period) factor table (id, period, weight_factor) as a data.frame — the frame-in/frame-out analogue of fit_weights_parquet().

Package

tters tters-package: tters: Sequential Target Trial Emulation Data Expansion