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)
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'))

Peer review:

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

gwashigh-dimensionalnon-local-priorvariable-selection

2.70 score 3 scripts 372 downloads 3 exports 45 dependencies

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

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-win-x86_64NOTENov 12 2024
R-4.5-linux-x86_64NOTENov 12 2024
R-4.4-win-x86_64NOTENov 12 2024
R-4.4-mac-x86_64NOTENov 12 2024
R-4.4-mac-aarch64NOTENov 12 2024
R-4.3-win-x86_64NOTENov 12 2024
R-4.3-mac-x86_64NOTENov 12 2024
R-4.3-mac-aarch64NOTENov 12 2024

Exports:GWASinlpsnlpsGLMnlpsLM

Dependencies:BHbigmemorybigmemory.sriclicodetoolsdplyrfansifastglmforeachgenericsglassoglmnetgluehorseshoeintervalsiteratorslatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmclustmgcvmombfmvtnormncvregnlmepillarpkgconfigpracmaR6RcppRcppArmadilloRcppEigenrlangshapesparseMatrixStatssurvivaltibbletidyselectutf8uuidvctrswithr