suddengains: Identify Sudden Gains in Longitudinal Data

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:	0.4.4
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
Suggests:	haven (≥ 2.1.0), writexl (≥ 1.1.0), knitr (≥ 1.21), DT (≥ 0.5), rmarkdown (≥ 1.11), spelling (≥ 2.1)
Published:	2020-05-22
Author:	Milan Wiedemann [aut, cre], Graham M Thew [ctb], 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:	GPL-3
URL:	https://milanwiedemann.github.io/suddengains/
NeedsCompilation:	no
Language:	en-US
Citation:	suddengains citation info
Materials:	README NEWS
CRAN checks:	suddengains results

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