Package: tters 0.1.1

tters: Sequential Target Trial Emulation Data Expansion (Rust + Polars Backend)

Fast, verified data-expansion stage for sequential target trial emulation, backed by a Rust and Polars engine via the 'extendr' crate. Reproduces, bit-for-bit, the expansion output of the 'TrialEmulation' R package. The heavy lifting lives in the 'tte-expand' Rust core crate; this package is a thin binding layer.

Authors:Michael Batech [aut, cre]

tters_0.1.1.tar.gz
tters_0.1.1.zip(r-4.7)tters_0.1.1.zip(r-4.6)tters_0.1.1.zip(r-4.5)
tters_0.1.1.tgz(r-4.6-x86_64)tters_0.1.1.tgz(r-4.6-arm64)tters_0.1.1.tgz(r-4.5-x86_64)tters_0.1.1.tgz(r-4.5-arm64)
tters_0.1.1.tar.gz(r-4.7-arm64)tters_0.1.1.tar.gz(r-4.7-x86_64)tters_0.1.1.tar.gz(r-4.6-arm64)tters_0.1.1.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
tters/json (API)

# Install 'tters' in R:
install.packages('tters', repos = c('https://oldschoolcool2.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/oldschoolcool2/rust-tte/issues

On CRAN:

Conda:

rustcargo

1.70 score 18 exports 2 dependencies

Last updated from:3edab88dd1 (on main). Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK1236
linux-devel-x86_64OK1284
source / vignettesOK1256
linux-release-arm64OK1229
linux-release-x86_64OK1266
macos-release-arm64OK1133
macos-release-x86_64OK2805
macos-oldrel-arm64OK1667
macos-oldrel-x86_64OK3117
windows-develOK2059
windows-releaseOK2520
windows-oldrelOK2636
wasm-releaseFAIL186

Exports:expand_dfexpand_parquetexpand_trialexpand_trial_dfexpand_trial_weightedexpand_trial_weighted_dfexpand_trial_weighted_fittedexpand_trial_weighted_fitted_dfexpand_trials_ttersexpand_weighted_dfexpand_weighted_fitted_dfexpand_weighted_fitted_parquetexpand_weighted_parquetfit_trial_weightsfit_trial_weights_dffit_weights_dffit_weights_parquetsave_to_tters

Dependencies:bitbit64

Readme and manuals

Help Manual

Help pageTopics
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_df
Expand a prepared person-time Parquet dataset into the sequential target-trial layout and write the result to 'output_path'.expand_parquet
Expand a target-trial person-time dataset (ergonomic wrapper)expand_trial
Expand a target-trial cohort data.frame in memory (ergonomic wrapper)expand_trial_df
Expand a dataset and attach pre-computed inverse-probability weightsexpand_trial_weighted
Expand a cohort data.frame and attach pre-computed weights, in memory (wrapper)expand_trial_weighted_df
Fit IPW weights and expand a cohort into a weighted trial frame (ergonomic wrapper)expand_trial_weighted_fitted
Fit IPW weights and expand a cohort data.frame in one call, in memory (wrapper)expand_trial_weighted_fitted_df
Expand a sequence of target trials with the Rust + Polars engineexpand_trials_tters
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_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.expand_weighted_fitted_df
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).expand_weighted_fitted_parquet
Expand a person-time Parquet dataset and attach pre-computed inverse-probability weights, writing the weighted frame to 'output_path'.expand_weighted_parquet
Fit inverse-probability weights for a target-trial cohort (ergonomic wrapper)fit_trial_weights
Fit inverse-probability weights for a cohort data.frame, in memory (wrapper)fit_trial_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()'.fit_weights_df
Fit the inverse-probability *weight factor* for a Parquet cohort in Rust and write the per-(id, period) factor table (id, period, weight_factor).fit_weights_parquet
Create a 'te_datastore_tters' storage backendsave_to_tters