| Name : R-EBglmnet
| |
| Version : 6.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.5
| Date : 2024-06-14 11:00:20
|
| Group : Development/Libraries/Other
| Source RPM : R-EBglmnet-6.0-lp153.1.5.src.rpm
|
| Size : 0.43 MB
| |
| Packager : (none)
| |
| Summary : Empirical Bayesian Lasso and Elastic Net Methods for Generalized Linear Models
|
Description :
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. Please
reference its use as A Huang and D Liu (2016) < doi:
10.1093/bioinformatics/btw143>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
