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Important note: this wiki is a work in progress.

'''swe''' is a tool for the analysis of longitudinal and repeated measures neuroimaging data based on the sandwich estimator.

'''swe''' fits a simple "marginal model" with no need for per-subject dummy variables, and instead of iterative computation of variance components it uses the non-iterative "sandwich estimator" to find standard errors.
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= Referencing =

{{{#!wiki references
Guillaume, B., Hua, X., Thompson, P. M., Waldorp, L., & Nichols, T. E. (2014). Fast and accurate modelling of longitudinal and repeated measures neuroimaging data. NeuroImage, 94, 287–302. [[https://doi.org/10.1016/j.neuroimage.2014.03.029|doi:10.1016/j.neuroimage.2014.03.029]]

Guillaume, B., Nichols, T. E. (2015). Non-parametric Inference for Longitudinal and Repeated-Measures Neuroimaging Data with the Wild Bootstrap. Poster presented at the Organization for Human Brain Mapping (OHBM) in Hawaii, 14-18, 2015. [[https://figshare.com/articles/Non-parametric_Inference_for_Longitudinal_and_Repeated-Measures_Neuroimaging_Data_with_the_Wild_Bootstrap/5478229|PDF]].

Guillaume, B. (2015). Accurate Non-Iterative Modelling and Inference of Longitudinal Neuroimaging Data. Thesis, Maastricht University / University of Liège. [[https://orbi.uliege.be/handle/2268/186284|PDF]].
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CategoryStatistics [[CategorySwe]]