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eider: Declarative Feature Extraction from Tabular Data Records
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.
Package details |
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| Author | Catalina Vallejos [ctb] (<https://orcid.org/0000-0003-3638-1960>), Louis Aslett [ctb] (<https://orcid.org/0000-0003-2211-233X>), Simon Rogers [ctb] (<https://orcid.org/0000-0003-3578-4477>), Camila Rangel Smith [cre, ctb] (<https://orcid.org/0000-0002-0227-836X>), Helen Duncan Little [aut] (<https://orcid.org/0000-0002-0897-7188>), Jonathan Yong [aut] (<https://orcid.org/0000-0002-2472-974X>), The Alan Turing Institute [cph, fnd] |
| Maintainer | Camila Rangel Smith <crangelsmith@turing.ac.uk> |
| License | MIT + file LICENSE |
| Version | 1.0.0 |
| URL | https://github.com/alan-turing-institute/eider |
| Package repository | View on CRAN |
| Installation |
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