Package: GWASBrewer 0.3.0.0238

Jean Morrison

GWASBrewer: Simulate Realistic GWAS Summary Statistics

Simulate GWAS summary statistics from specified DAG or factor structure.

Authors:Jean Morrison

GWASBrewer_0.3.0.0238.tar.gz
GWASBrewer_0.3.0.0238.zip(r-4.7)GWASBrewer_0.3.0.0238.zip(r-4.6)GWASBrewer_0.3.0.0238.zip(r-4.5)
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GWASBrewer_0.3.0.0238.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GWASBrewer/json (API)

# Install 'GWASBrewer' in R:
install.packages('GWASBrewer', repos = c('https://mrcieu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jean997/gwasbrewer/issues

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

On CRAN:

Conda:

openblascpp

5.15 score 12 stars 26 scripts 22 exports 27 dependencies

Last updated from:8ab3b99ce5. Checks:11 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE208
linux-devel-x86_64NOTE223
source / vignettesOK329
linux-release-arm64NOTE275
linux-release-x86_64NOTE212
macos-release-arm64NOTE143
macos-release-x86_64NOTE233
macos-oldrel-arm64NOTE181
macos-oldrel-x86_64NOTE367
windows-develNOTE210
windows-releaseNOTE236
windows-oldrelNOTE194
wasm-releaseOK162

Exports:compute_h2fast_eigenfast_eigen_valsfast_lmfixed_to_scale_famgen_genos_mvngenerate_F_simplegenerate_F2generate_random_Fhapsim_simplemixnorm_to_scale_famresample_inddataresample_sumstatsrescale_sumstatsrnormalmixsim_extract_ldsim_ld_proxysim_ld_prunesim_lfsim_mvsim_mv_determinedxyz_to_G

Dependencies:clicpp11dplyrgenericsgluelatticelifecyclemagrittrMASSMatrixpillarpkgconfigplyrpurrrR6RcppRcppArmadilloreshape2rlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Simulating Data
Introduction | Introduction to sim_mv | Basic Usage | Input | Output | Simplest Usage | Specifying Causal Relationships Between Traits | Specifying Allele Frequencies | Simulating Data with LD | LD-Pruning, LD-Proxies, and LD Matrix Extraction | Specifying Sample Size, Sample Overlap, and Environmental Correlation | Specifying Sample Size and Sample Overlap | Using Sample Size 0 to Omit Traits | Understanding Genetic and Environmental Covariance
Rendered fromsimulating_data.Rmdusingknitr::rmarkdown

Last update: 2024-04-07
Started: 2022-08-24

Controlling Effect Size Distributions
Introduction | Default Behavior | Controlling which Variants are Effect Variants | Controlling Effect Size Distribution | Drawing Effects from a Mixture of Normals | Providing a fixed list of relative effect sizes | Providing an Exact Set of Direct Effects | Different effect distributions for different traits | Example Using Pre-Specified Effects | Custom Effect Size Distributions
Rendered fromeffect_distribution.Rmdusingknitr::rmarkdown

Last update: 2024-03-27
Started: 2023-04-17

Resampling and Re-Scaling Summary and Individual Level Data
Introduction | Resampling Summary Statistics or Individual Level Data from the Same Population | Resampling Summary Statistics from the Same Population | Resampling Individual Level Data from the Same Population | Generating genotypes only | Generating Genotypes and Phenotypes | Generating Phenotypes Only | Resampling Data from a Different Population | Understanding Effect Size Units | Changing LD and Allele Frequencies | Changing Environmental Variance or Covariance | Rescaling Effect Size Units
Rendered fromresampling.Rmdusingknitr::rmarkdown

Last update: 2024-03-27
Started: 2023-08-23