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suddengains

Identify Sudden Gains in Longitudinal Data

Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) 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.

News

suddengains 0.2.0

  • Add new plot function plot_sg_trajectories() to plot trajectories of individual cases
  • Add more specific warning and error messages:
    • Message notification if no gains/losses are identified using: identify_sg() or identify_sl()
    • Error if no gains/losses are identified using: create_bysg(), create_byperson()
  • Add colour option to plot_sg() function for different groups
  • Fix calculation of start and end values in plot_sg() function for bysg data sets
  • Update help files
    • Fix spelling mistakes
    • Clarify some arguments
    • Add table explaining "pattern" method to select_cases() function

suddengains 0.1.0

First CRAN submission.

Reference manual

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install.packages("suddengains")

0.4.4 by Milan Wiedemann, 9 months ago

https://milanwiedemann.github.io/suddengains/

Report a bug at https://github.com/milanwiedemann/suddengains/issues

Browse source code at https://github.com/cran/suddengains

Authors: Milan Wiedemann [aut, cre] , Graham M Thew [ctb] , Richard Stott [ctb] , Anke Ehlers [ctb, ths] , Mental Health Research UK [fnd] , Wellcome Trust [fnd]

Documentation:   PDF Manual  

GPL-3 license

Imports dplyr, tibble, magrittr, rlang, stringr, ggplot2, psych, readr, tidyr, ggrepel, patchwork, forcats, naniar, scales

Suggests haven, writexl, knitr, DT, rmarkdown, spelling

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