bgsmtr
R package bgsmtr: Bayesian Group Sparse Multi-Task Regression. Implementation of Bayesian multi-task regression models and was developed within the context of imaging genetics. The package can currently fit two models. The Bayesian group sparse multi-task regression model of Greenlaw et al. (2017)<doi:10.1093/bioinformatics/btx215> can be fit with implementation using Gibbs sampling. An extension of this model developed by Song, Ge et al. to accommodate both spatial correlation as well as correlation across brain hemispheres can also be fit using either mean-field variational Bayes or Gibbs sampling. The model can also be used more generally for multivariate (non-imaging) phenotypes with spatial correlation.
Keywords for this software
References in zbMATH (referenced in 3 articles )
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Sorted by year (- Nie, Yunlong; Opoku, Eugene; Yasmin, Laila; Song, Yin; Wang, Jie; Wu, Sidi; Scarapicchia, Vanessa; Gawryluk, Jodie; Wang, Liangliang; Cao, Jiguo; Nathoo, Farouk S.: Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics (2020)
- Ning, Bo; Jeong, Seonghyun; Ghosal, Subhashis: Bayesian linear regression for multivariate responses under group sparsity (2020)
- Yang, Xinming; Narisetty, Naveen N.: Consistent group selection with Bayesian high dimensional modeling (2020)