MACS
MACS – a new SPM toolbox for model assessment, comparison and selection
This toolbox (pronounced as "Max") evaluates general linear models (GLMs) for functional magnetic resonance imaging (fMRI) data estimated in Statistical Parametric Mapping (SPM). MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection [1] and model averaging [2] in fMRI data analysis [3].
This is MACS V1.3, also referred to as MACS R2018b, released on 31/12/2018. The developers intend to immediately commit bug fixes to this repository and provide a general update two times a year. A toolbox paper has been published in a peer-reviewed journal [3] and a toolbox manual is included in the repository [4].
To install the toolbox, it has to be downloaded and placed as a subdirectory "MACS" into the SPM toolbox folder. Upon starting SPM, batch modules for toolbox features can be accessed by clicking "SPM -> Tools -> MACS Toolbox" in the SPM batch editor [3, Fig. 3; 4, Fig. 1]. MACS is optimized for SPM12, but also compatible with SPM8.
The repository includes a number of sub-directories:
MACS_Examples: SPM batch editor job files for example analyses from the toolbox paper [3, Sec. 4]MACS_Pipelines: SPM template batches/script for cvBMS [1], cvBMA [2] and model space definitionMACS_Extensions: MATLAB scripts for toolbox extensions as described in the manual [4, Sec. 15]MACS_Manual: TEX and PDF file belonging to the latest version of the toolbox manual
[1] https://www.sciencedirect.com/science/article/pii/S1053811916303615
[2] https://www.sciencedirect.com/science/article/pii/S105381191730527X
[3] https://www.sciencedirect.com/science/article/pii/S0165027018301468
[4] https://github.com/JoramSoch/MACS/blob/master/MACS_Manual/Manual.pdf