Added compatibility with both nilearn 0.8 and 0.9+
0.7
New feature: it is now possible to filter out small clusters from the dypac analysis. By default clusters that are smaller than 50% of the cluster size of a uniform solution (i.e. all culsters have same size) get excluded. This removes some noise as well as estimates of dwell times.
0.6
Addition of two masker classes: LabelsMasker and MapsMasker, and accompanying tutorial notebooks. These classes can be used to generate dypac-style maskers for any type of hard or soft parcellation.
What's Changed * Update nilearn version to 0.9.0 by htwangtw in https://github.com/courtois-neuromod/dypac/pull/85 * Iss83 masker by pbellec in https://github.com/courtois-neuromod/dypac/pull/86
New Contributors * htwangtw made their first contribution in https://github.com/courtois-neuromod/dypac/pull/85
Bug fix release: the confounds were not properly handled by MultiNiftiMasker, and not regressed out of the data prior to estimating the parcels.
0.5.2
Grey matter segmentation is now used to constrain the brain mask, instead of re-weighting signals. This is accessible through the `grey_matter` and `threshold_grey_matter` arguments, and can be skipped by specifying `grey_matter=None`.
0.5.1
Minor update: * consensus clustering using `k_means` now uses the `sample_weight` argument, set to the dwell times of the parcellations being aggregated.