Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python.
|Depends:||R (≥ 3.0)|
|Imports:||lubridate (≥ 1.5), parallel (≥ 3.0), data.table (≥ 1.9), rsvg (≥ 1.3), jpeg (≥ 0.1), png (≥ 0.1), webp (≥ 1.0), methods|
|Suggests:||rmarkdown (≥ 1.1), knitr (≥ 1.20), R.rsp (≥ 0.40), base64 (≥ 2.0), AdhereRViz (≥ 0.2)|
|Author:||Dan Dediu [aut, cre], Alexandra Dima [aut], Samuel Allemann [aut]|
|Maintainer:||Dan Dediu <ddediu at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|Citation:||AdhereR citation info|
|CRAN checks:||AdhereR results|
AdhereR: Adherence to Medications
Calling AdhereR from Python3
Using AdhereR with various database technologies for processing very large datasets
|Windows binaries:||r-devel: AdhereR_0.7.0.zip, r-release: AdhereR_0.7.0.zip, r-oldrel: AdhereR_0.7.0.zip|
|macOS binaries:||r-release (arm64): AdhereR_0.7.0.tgz, r-oldrel (arm64): AdhereR_0.7.0.tgz, r-release (x86_64): AdhereR_0.7.0.tgz, r-oldrel (x86_64): AdhereR_0.7.0.tgz|
|Old sources:||AdhereR archive|
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