Several changes and additions are made: A more comprehensive docstring is used to enhance information on PyReliMRI readthedocs The ICC function tests were expanded to confirm ICC, between subject variance and within subject variance estimates from sumqc_icc() are compared to estimates from liner mixed effect model outputs from stat models. Specifically, see test comparing pyrelimri.icc vs statsmodels The returned list of estimates for ICC computations have been revised. Previously, the MSBS and MSWS were returned. However, this was not always informative to the ICC being computed, nor to understanding how variances are impacted. For example, when the ICC(3,1) was returned, the MSBS and MSWS were not directly used in the computation. Furthermore, they were not true to the denominator and the numerator for each formula. Thus, for voxelwise, edgewise, rois, etc., where sumsq_icc() is used, now a dictionary is returned with: 1) ICC estimate, 2) upper 95CI, 3) lower 95CI, 4) between subject variance, 5) within subject variance, 6) between measure variance (in case of ICC(2,1), otherwise None/empty values) Included a TR-by-TR timeseries extraction for masks/coordinates and locked to onset times/behavioral events. This provides the extraction of the mean signal change across a timeseries for a given ROI. This is based on Nilearn's niftimaskers where standardizing via the call 'psc' Included an edgewise_icc computation on correlation matrices. This is strictly for comparing a list of lists of variables includes subjects' correlation matrices across runs/sessions or the paths to these correlation matrices. Note, if using pandas dataframe standards, it is assumed that the header = None and row index = None