susieR-package: susieR: Sum of Single Effects Linear Regression

In susieR: Sum of Single Effects Linear Regression

susieR: Sum of Single Effects Linear Regression

Description

Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) doi: 10.1101/501114 and Zou et al (2021) doi: 10.1101/2021.11.03.467167. These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).

Author(s)

Maintainer: Peter Carbonetto [e-mail]

Authors:

• Gao Wang [e-mail]

• Yuxin Zou

• Kaiqian Zhang

• Matthew Stephens

See Also

Useful links:

• https://github.com/stephenslab/susieR

• Report bugs at https://github.com/stephenslab/susieR/issues

susieR documentation built on March 7, 2023, 6:11 p.m.