Package: GWASinlps 2.2

GWASinlps: Non-Local Prior Based Iterative Variable Selection Tool for Genome-Wide Association Studies

Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <doi:10.1093/bioinformatics/bty472>).

Authors:Nilotpal Sanyal [aut, cre]

GWASinlps_2.2.tar.gz
GWASinlps_2.2.zip(r-4.5)GWASinlps_2.2.zip(r-4.4)GWASinlps_2.2.zip(r-4.3)
GWASinlps_2.2.tgz(r-4.4-x86_64)GWASinlps_2.2.tgz(r-4.4-arm64)GWASinlps_2.2.tgz(r-4.3-x86_64)GWASinlps_2.2.tgz(r-4.3-arm64)
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GWASinlps.pdf |GWASinlps.html✨
GWASinlps/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/nilotpalsanyal/gwasinlps/issues

On CRAN:

gwashigh-dimensionalnon-local-priorvariable-selection

3 exports 0.64 score 45 dependencies 3 scripts 329 downloads

Last updated 2 years agofrom:8dd56007c1. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:GWASinlpsnlpsGLMnlpsLM

Dependencies:BHbigmemorybigmemory.sriclicodetoolsdplyrfansifastglmforeachgenericsglassoglmnetgluehorseshoeintervalsiteratorslatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmclustmgcvmombfmvtnormncvregnlmepillarpkgconfigpracmaR6RcppRcppArmadilloRcppEigenrlangshapesparseMatrixStatssurvivaltibbletidyselectutf8uuidvctrswithr