BLMM: Parallelised computing for big linear mixed models
<p>Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs. As sample sizes grow, researchers are presented with new opportunities to detect and account for grouping fa...
Main Authors: | Maullin-Sapey, T, Nichols, TE |
---|---|
Format: | Journal article |
Language: | English |
Published: |
Elsevier
2022
|
Similar Items
-
BLMM: Parallelised computing for big linear mixed models
by: Thomas Maullin-Sapey, et al.
Published: (2022-12-01) -
Fisher Scoring for crossed factor linear mixed models
by: Maullin-Sapey, T, et al.
Published: (2021) -
Framework for parallelisation on big data.
by: Lukman Ab Rahim, et al.
Published: (2019-01-01) -
SYSTEM PARALLELISATION FOR COMPUTER VISION
by: Asaad A. M. AL-Sudani
Published: (2003-06-01) -
Spatial confidence regions for combinations of excursion sets in image analysis
by: Maullin-Sapey, T, et al.
Published: (2023)