Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Version: 0.7.2 Depends: R (≥ 3.5.0) Imports: dplyr (≥ 0.8.0), tibble (≥ 2.1.1), magrittr (≥ 1.5), rlang (≥ 0.3.4), stringr (≥ 1.4.0), ggplot2 (≥ 3.1.1), psych (≥ 1.8.12), readr (≥ 1.3.1), tidyr (≥ 0.8.2), ggrepel (≥ 0.8.0), patchwork (≥ 1.0.0), forcats, naniar, scales, cli Suggests: haven (≥ 2.1.0), writexl (≥ 1.1.0), knitr (≥ 1.21), DT (≥ 0.5), rmarkdown (≥ 1.11), spelling (≥ 2.1) Published: 2023-02-28 DOI: 10.32614/CRAN.package.suddengains Author: Milan Wiedemann [aut, cre], Graham M Thew [aut], Richard Stott [ctb], Anke Ehlers [ctb, ths], Mental Health Research UK [fnd], Wellcome Trust [fnd] Maintainer: Milan Wiedemann <milan.wiedemann at gmail.com> BugReports: https://github.com/milanwiedemann/suddengains/issues License: MIT + file LICENSE URL: https://milanwiedemann.github.io/suddengains/ NeedsCompilation: no Language: en-US Citation: suddengains citation info Materials: README NEWS CRAN checks: suddengains resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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