The R-package hierinf offers tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel.
Installation
You can install the development version from Github by running
hierinf
The R-package hierinf offers tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel.
Installation
You can install the development version from Github by running
References
Renaux, C. et al. (2018), Hierarchical inference for genome-wide association studies: a view on methodology with software. (arXiv:1805.02988)