Package: GenomicSEM 0.0.5

Andrew Grotzinger

GenomicSEM: Structural equation modeling based on GWAS summary statistics

Later

Authors:Andrew Grotzinger, Matthijs van der Zee, Mijke Rhemtulla, Hill Ip, Michel Nivard, Elliot Tucker-Drob

GenomicSEM_0.0.5.tar.gz
GenomicSEM_0.0.5.zip(r-4.7)GenomicSEM_0.0.5.zip(r-4.6)GenomicSEM_0.0.5.zip(r-4.5)
GenomicSEM_0.0.5.tgz(r-4.6-any)GenomicSEM_0.0.5.tgz(r-4.5-any)
GenomicSEM_0.0.5.tar.gz(r-4.7-any)GenomicSEM_0.0.5.tar.gz(r-4.6-any)
GenomicSEM_0.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GenomicSEM/json (API)

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

Bug tracker:https://github.com/genomicsem/genomicsem/issues

On CRAN:

Conda:

7.60 score 278 stars 182 scripts 25 exports 57 dependencies

Last updated from:0a63ac0ea0. Checks:7 WARNING, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING173
source / vignettesOK197
linux-release-x86_64WARNING170
macos-release-arm64WARNING117
macos-oldrel-arm64WARNING113
windows-develWARNING148
windows-releaseWARNING151
windows-oldrelWARNING137
wasm-releaseOK109

Exports:addGenesaddSNPscommonfactorcommonfactorGWASenrichhdlindexSldsclocalSRMDmultiGenemultiSNPmungepaLDSCQTraitread_fusionrgmodels_ldscsimLDSCsubSVsummaryGLSsummaryGLSbandssumstatsuserGWASusermodelwrite.model

Dependencies:bitbit64classclicliprcodetoolscolorspacecpp11crayondata.tabledoParalleldplyre1071foreachgdatagenericsgluegridBasegtoolshmsiteratorslatticelavaanlifecyclemagrittrMASSMatrixmgsubmnormtnumDerivpbivnormpillarpkgconfigplyrprettyunitsprogressproxyquadprogR.methodsS3R.ooR.utilsR6Rcppreadrrlangsfsmiscsimsalaparsplitstackshapestringistringrtibbletidyselecttzdbutf8vctrsvroomwithr

Readme and manuals

Help Manual

Help pageTopics
k' is the number of variables in the model fit' is the fit function of the regression model names' is a vector of variable names in the order you used.rearrange
Combine LDSC and summary statistic output for multivariate GWAS using GenomicSEMaddSNPs
Run common factor model on genetic covariance and sampling covariance matrixcommonfactor
Estimate SNP effects on a single common factorcommonfactorGWAS
Estimate enrichment of model parameter for a user specified modelenrich
estimate a genetic covariance matrix using High Definition Likelihood (HDL) estimation in Rhdl
build a convariance structure using LD score regression in Rldsc
localSRMD for Genomic measurement invariance modelslocalSRMD
Combine LDSC, summary statistic output, and LD information for models including multiple SNPsmultiSNP
Clean and munge files to enable LD score regressionmunge
Parallel Analysis Based on Multivariate LDSCpaLDSC
Run the QTrait function to test trait-specific heterogeneity in genetic correlation modelsQTrait
Format univariate FUSION TWAS output across multiple traits for subsequent use in a multivariate TWAS [T-SEM]read_fusion
Estimate a model-implied genetic covariance matrixrgmodel
Estimate genetic covariance matrices within functional annotations using multivariable Stratified LD score regressions_ldsc
Simulate GWAS summary statistics for multivariate LDSCsimLDSC
Allign summary statistics from univariate GWAS for a GWAS in GenomicSEMsumstats
Create genetic covariance matrices for individual SNPs and estimate SNP effects for a user specified multivariate GWASuserGWAS
Run user specified model on LDSC outputusermodel
Automate writing model syntax using EFA outputwrite.model