Hierarchical inference testing (HIT) for (generalized) linear models with correlated covariates applicable to high-dimensional settings.
Hierarchical inference testing (HIT) for (generalized) linear models with correlated covariates. HIT is furthermore applicable to high-dimensional settings. For details see:
Mandozzi, J. and Buehlmann, P. (2015). Hierarchical testing in the high-dimensional setting with correlated variables. Journal of the American Statistical Association. Preprint
Klasen, J. R. et al. (2016). A multi-marker association method for genome-wide association studies without the need for population structure correction. Nature Communications. Paper
The package can be installed from CRAN,
, if you haven't
devtools installed yet you have to do so first.
hit-function example gives an overview of the functionality of the
package and can be accessed once the package is loaded.
hit: second path for lambda estimation, with a new lambda for each sample split.
reorder: a more general reorder function for hierarchy
hit: remove the alpha optimization and allow only one alpha value
hit: allow the response variable (y) to be Poisson distributed
summary.hit: bug fix
hit: bug fixes in summary and some other small fixes
hit: sticks to cross-validation as selection method
hit: arguments have changed
fast.anova: a new
fast.glmanova method for GLM's
hit: now is able to deal with binomial responses
hit: a new selection method for the active set