Data

Tools

Indexes

Applications

Experiments

Open Source Science

macs

MACS – a new SPM toolbox for model assessment, comparison and selection

https://github.com/joramsoch/macs

Science Score: 41.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: sciencedirect.com, zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

MACS – a new SPM toolbox for model assessment, comparison and selection

Basic Info
  • Host: GitHub
  • Owner: JoramSoch
  • License: gpl-3.0
  • Language: MATLAB
  • Default Branch: master
  • Size: 1.08 MB
Statistics
  • Stars: 21
  • Watchers: 3
  • Forks: 5
  • Open Issues: 3
  • Releases: 4
Created about 9 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

MACS

DOI

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 definition - MACS_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

Owner

  • Name: Joram Soch
  • Login: JoramSoch
  • Kind: user
  • Location: Berlin, Germany
  • Company: Bernstein Center for Computational Neuroscience

Postdoc at BCCN Berlin. Interested in cognitive neuroscience, fMRI data analysis and Bayesian model selection. Developer of MACS for SPM (see right).

Citation (CITATION.md) 

If you use this toolbox, please cite the following paper:

- Soch J, Allefeld C (2018). MACS – a new SPM toolbox for model assessment, comparison and selection. <i>Journal of Neuroscience Methods</i>, vol. 306, pp. 19-31; DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0165027018301468">10.1016/j.jneumeth.2018.05.017</a>.

If you use cvBMS or cvBMA, please also cite the respective method:

- Soch J, Haynes JD, Allefeld C (2016). How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection. <i>NeuroImage</i>, vol. 141, pp. 469-489; DOI: <a href="https://www.sciencedirect.com/science/article/pii/S1053811916303615">10.1016/j.neuroimage.2016.07.047</a>.
- Soch J, Meyer AP, Haynes JD, Allefeld C (2017). How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging. <i>NeuroImage</i>, vol. 158, pp. 186-195; DOI: <a href="https://www.sciencedirect.com/science/article/pii/S105381191730527X">10.1016/j.neuroimage.2017.06.056</a>.

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1