Title: Gaussian Mixture Modeling Algorithms And The Belief-based
Description: Two partially supervised mixture modeling methods:
soft-label and belief-based modeling are implemented.

Title: Gaussian Mixture Modeling Algorithms and the Belief-Based
Uncertain Knowledge, Journal of Statistical Software

Title: Bayesian Group Sparse Multi-Task Regression
Description: Fits a Bayesian group-sparse multi-task regression model using Gibbs
sampling. The hierarchical prior encourages shrinkage of the estimated regression
coefficients at both the gene and SNP level. The model has been applied
successfully to imaging phenotypes of dimension up to 100; it can be used more
generally for multivariate (non-imaging) phenotypes.
Author: Farouk S. Nathoo, Keelin Greenlaw, Mary Lesperance
Maintainer: Farouk S. Nathoo <nathoo@uvic.ca>
Packaged: 2016-10-04 14:23:21 UTC; farouknathoo

coefficients at both the gene and SNP level. The model has been extended to a
spatial model that allows for two type correlation in neuroimaging genetics data and
been applied successfully to imaging phenotypes of dimension up to 100; it can be used
more generally for multivariate (non-imaging) phenotypes.
Author: Yin Song, Shufei Ge, Liangliang Wang, Farouk S. Nathoo, Keelin Greenlaw, Mary Lesperance
0.3.15),LaplacesDemon (>= 16.1.0), TargetScore (>=

0.3.15),LaplacesDemon (>= 16.1.0), CholWishart (>=

Description: 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) can be fit with implementation using Gibbs sampling. An extension of this model developed by Song, Ge et al. (2019) 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.
Author: Yin Song, Shufei Ge, Liangliang Wang, Jiguo Cao, Keelin Greenlaw, Mary Lesperance, Farouk S. Nathoo