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  "Title": "Bayesian Hierarchical Multi-Subject Multiscale Analysis of\nFunctional MRI (fMRI) Data",
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  "Date": "2025-09-23",
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  "Description": "Package BHMSMAfMRI performs Bayesian hierarchical\nmulti-subject multiscale analysis of fMRI data as described in\nSanyal & Ferreira (2012)\n<DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale\ndata, using wavelet-based prior that borrows strength across\nsubjects and provides posterior smoothed images of the effect\nsizes and samples from the posterior distribution.",
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      "title": "Bayesian Hierarchical Multi-Subject Multiscale Analysis (BHMSMA) of Functional MRI Data",
      "topics": [
        "BHMSMAfMRI"
      ]
    },
    {
      "page": "BHMSMA",
      "title": "Bayesian hierarchical multi-subject multiscale analysis (BHMSMA) of functional MRI data or other multiscale data",
      "topics": [
        "BHMSMA"
      ]
    },
    {
      "page": "fmridata",
      "title": "A simulated fMRI data for 3 subjects",
      "topics": [
        "fmridata"
      ]
    },
    {
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      "title": "Fit GLM (general linear model) to the fMRI time-series of all voxels within a single 2D brain slice",
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        "glmcoef"
      ]
    },
    {
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      ]
    },
    {
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      "title": "Obtain posterior estimates of the BHMSMA wavelet coefficients",
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        "The main function BHMSMA",
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        "Wavelet transform of the GLM coefficients: waveletcoef",
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        "Computing posterior distribution of the wavelet coefficients: postmixprob, postwaveletcoef",
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