# install.packages("remotes")
remotes::install_github("AmeliaES/TIDAL")
library("TIDAL")
# Launch the R Shiny app
launchTIDAL()

The aim is for this digital tool to facilitate trajectories work and remove barriers to implementing longitudinal research to researchers without specialist statistical backgrounds. The following pages guide trajectory modelling and capture clinically meaningful features from mental health trajectories for specific individuals and/or specific groups of people.

This allows the user to upload a wide format of their longitudinal dataset. Select which columns measure time and the phenotype they want to model trajectories on. Converts the dataframe to long format. Allows the user to download the long format dataset.

This is the first stage of the trajectory modelling. Here the user either uploads a long format dataset or uses the dataset formatted on the previous page (Data Preparation). They specify the columns relatated to the variables to include in the model. There is a choice of model type and the user can see which model type looks like it best fits their data to explore further on the following pages.

Split the trajectories by categorical varaibles to examine the differences in trajectories.

View trajectories for specific individuals. Choose from a random sample, specific individuals of interest, individuals within a specific variable, eg. a random sample of females only.

• Points of acceleration
  • Examine timing of peak velocity of trajectories. This feature highlights a critical period at which further support or interventions could be introduced to dramatically shift an individual’s illness trajectory.
• Stability
  • Captures within-individual variability in depressive symptoms over time and compare how this varies by different forms of interventions or combinations of interventions.
• Allow users to input an x-axis value (eg. age) and recieve y-axis value (eg. depression score), for mean values from a user specified model.
• Allow users to download tables and plots (also to edit colours in the plots)
• Return an R script at the end of analysis with the code ran to generate tables and plots downloaded.