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:
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)
GWASinlps_2.2.tar.gz(r-4.5-noble)GWASinlps_2.2.tar.gz(r-4.4-noble)
GWASinlps_2.2.tgz(r-4.4-emscripten)GWASinlps_2.2.tgz(r-4.3-emscripten)
GWASinlps.pdf |GWASinlps.html✨
GWASinlps/json (API)
NEWS
# Install 'GWASinlps' in R: |
install.packages('GWASinlps', repos = c('https://nilotpalsanyal.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nilotpalsanyal/gwasinlps/issues
gwashigh-dimensionalnon-local-priorvariable-selection
Last updated 2 years agofrom:8dd56007c1. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 12 2024 |
R-4.5-win-x86_64 | NOTE | Nov 12 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 12 2024 |
R-4.4-win-x86_64 | NOTE | Nov 12 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 12 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 12 2024 |
R-4.3-win-x86_64 | NOTE | Nov 12 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 12 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 12 2024 |
Exports:GWASinlpsnlpsGLMnlpsLM
Dependencies:BHbigmemorybigmemory.sriclicodetoolsdplyrfansifastglmforeachgenericsglassoglmnetgluehorseshoeintervalsiteratorslatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmclustmgcvmombfmvtnormncvregnlmepillarpkgconfigpracmaR6RcppRcppArmadilloRcppEigenrlangshapesparseMatrixStatssurvivaltibbletidyselectutf8uuidvctrswithr