Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <doi:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <doi:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <doi:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
|Depends:||R (≥ 3.1.1), mvtnorm (≥ 1.0-0)|
|Imports:||MASS, mnormt, Rcpp (≥ 0.12.6)|
|Author:||Chong Wu and Wei Pan|
|Maintainer:||Chong Wu <wuxx0845 at umn.edu>|
|CRAN checks:||GLMaSPU results|
|Windows binaries:||r-devel: GLMaSPU_1.0.zip, r-release: GLMaSPU_1.0.zip, r-oldrel: GLMaSPU_1.0.zip|
|OS X Mavericks binaries:||r-release: GLMaSPU_1.0.tgz, r-oldrel: GLMaSPU_1.0.tgz|
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