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)<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.

Description: Bimodal Gumbel distribution. General functions for performing extreme value analysis.
Author: Pedro C. Brom [aut, cre, cph] (<https://orcid.org/0000-0002-1288-7695>,
Marcelo B. Pereira [aut, cph] (<https://orcid.org/0000-0002-1182-5193>)
Maintainer: Pedro C. Brom <pcbrom@gmail.com>

Imports: MCMCpack, MASS, quantreg, SparseM, coda
SystemRequirements: gcc (>= 4.0), gfortran, clang++
Marcelo B. Pereira [aut, cph] (<https://orcid.org/0000-0002-1182-5193>,

Imports: MCMCpack, MASS, quantreg, SparseM, coda, stats

Maintainer: Bingshu E. Chen <bingshu.chen@queensu.ca>
Description: Biomarker threshold models are tools to fit both predictive and prognostic biomarker effects. Both generalized linear models and Cox proportional hazards models can be fitted using either Bayesian method or profile likelihood method.

Description: Biomarker threshold models are tools to fit both predictive and prognostic biomarker effects.

Description: Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

Depends: R (>= 2.10.0), coda, MASS, survival

Depends: R (>= 3.5.0), coda, ggplot2, MASS, survival
Description: Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.