Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.
Version: 1.0.0 Imports: dplyr, lubridate, stringr, magrittr, jsonlite, logger, purrr, fs, tibble, rlang Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), tidyr Published: 2024-05-13 DOI: 10.32614/CRAN.package.eider Author: Catalina Vallejos [ctb], Louis Aslett [ctb], Simon Rogers [ctb], Camila Rangel Smith [cre, ctb], Helen Duncan Little [aut], Jonathan Yong [aut], The Alan Turing Institute [cph, fnd] Maintainer: Camila Rangel Smith <crangelsmith at turing.ac.uk> BugReports: https://github.com/alan-turing-institute/eider/issues License: MIT + file LICENSE URL: https://github.com/alan-turing-institute/eider NeedsCompilation: no Materials: README NEWS CRAN checks: eider resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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