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## LOCUS – Large-scale variational inference for Bayesian variable selection in multiple-response regression  <img src="man/figures/locus_logo.png" align="right" height="150"/>

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## Overview

**locus** is an R package providing efficient variational algorithms for
simultaneous variable selection of covariates and associated responses based
on multivariate regression models. Dependence across responses linked to the 
same covariates is captured through the model hierarchical structure 
(H. Ruffieux, A. C. Davison, J. Hager, I. Irincheeva, Efficient inference 
for genetic association studies with multiple outcomes, *Biostatistics*, 
18:618–636, 2017). 

## Installation

To install, run the following command in R:

``` r
if(!require(remotes)) install.packages("remotes")
remotes::install_github("hruffieux/locus")
```

## Algorithms

The algorithms for joint covariate and response selection provided in **locus**
implement inference for regression models with 

* identity link;
* logistic link;
* probit link;
* identity-probit link.

Inference on models for group selection and based on a spatial Gaussian process to 
encode the dependence structure of the candidate predictors are also implemented. 
Moreover, covariate-level external information variables can be incorporated to 
inform the selection.

## License and authors

This software uses the GPL v2 license, see [LICENSE](LICENSE).
Authors and copyright are provided in [DESCRIPTION](DESCRIPTION). Loris Michel
has also contributed to the development of this project.

Please cite the software using the following reference: 
H. Ruffieux, A. C. Davison, J. Hager, I. Irincheeva, Efficient inference 
for genetic association studies with multiple outcomes, *Biostatistics*, 
18:618–636, 2017. 

## Issues

To report an issue, please use the [locus issue tracker](https://github.com/hruffieux/locus/issues) at github.com.