Perform multistage deterministic linkages, apply case definitions to datasets, and deduplicate records.
Records (rows) from datasets are linked by different matching criteria and sub-criteria (columns) in a specified order of certainty.
The linkage process handles missing data and conflicting matches based on this same order of certainty.
For episode grouping, rows of dated events (e.g. sample collection) or interval of events (e.g. hospital admission) are
grouped into chronological episodes beginning with a "Case". The process permits several options such as
episode lengths and recurrence periods which are used to build custom preferences for case assignment (definition).
The record linkage and episode grouping processes assign unique group IDs to matching records or those grouped into episodes.
This then allows for record deduplication or sub-analysis within these groups.
methods, utils, rlang, dplyr (>= 0.7.5), lubridate (>= 1.7.4),
tidyr (>= 0.8.2)

phonics, ggplot2, janitor, knitr, rmarkdown, testthat, covr

methods, grDevices, graphics, utils, dplyr (>= 0.7.5)
stringdist, knitr, rmarkdown, testthat, covr

Perform multistage deterministic linkages and apply case definitions to datasets.
Records are linked by different matching criteria in a specified order of certainty.
The linkage process handles missing data and conflicting matches using this same order.
Track events (e.g. sample collection) and period (e.g. hospital admission) to episodes.
This process permits several options such as episode lengths and recurrence.
Record linkage and episode tracking assign unique group identifiers to matched records.
Duplicate events or records can then be identified or sub-analyses performed within each group.
methods, grDevices, graphics, utils, Rfast

Record Linkage and Epidemiological Case Definitions in R
An R package for record linkage and implementing epidemiological case definitions in R.
Record linkage is implemented either through a multistage deterministic approach or a probabilistic approach.
Matching records are assigned to unique groups. There are mechanisms to address missing data and conflicting matches across linkage stages.
Track and assign events (e.g. sample collection) and periods (e.g. hospital admission) to unique groups based on a case definition.
The tracking process permits several options such as episode lengths and recurrence.
Duplicate events or records can then be identified for removal or further analyses.
methods, grDevices, graphics, utils, Rfast, ggplot2, rlang
knitr, rmarkdown, testthat, covr, stringdist