I’m attempting to complete my Masters project but I’m running into an issue with conducting a mass univariate longitudinal analysis. I have a R data frame consisting of 8000 columns (essentially 8,000) variables. Each column is essentially a list of numbers ( ranging from -1 to 1) for each participant from a study. These numbers inform me about the local efficiency of a particular node within particular region of the brain (this is graph theory information). So all of these 8,000 columns are related and I need to do a mass univariate analysis for multiple correction. Additionally, the dataset is in long format because each participant has a pre and post measure of local efficiency from the study.
Unless there is a better analysis/way to approach this, does anyone know of any software, r packages, or python packages, etc, that can run a longitudinal mass univariate analysis? I found some packages where you can specify a matrix of outcomes and your matrix of covariates that you want to use but no repeated measure option. I need to also include random effects in my analysis. Running 8000 individual mixed model regressions will be time-consuming and I will need a way to correct for all of those models.