Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
|Author||Anhui Huang, Dianting Liu|
|Maintainer||Anhui Huang <[email protected]>|
|Package repository||View on CRAN|
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