mrcieu r-universe repositoryhttps://mrcieu.r-universe.devPackage updated in mrcieucranlike-server 0.18.21https://github.com/mrcieu.png?size=400mrcieu r-universe repositoryhttps://mrcieu.r-universe.devFri, 09 Aug 2024 08:03:05 GMT[mrcieu] midoc 0.0.0.9000elinor.curnow@bristol.ac.uk (Elinor Curnow)A guidance system for analysis with missing data. It
incorporates expert, up-to-date methodology to help researchers
choose the most appropriate analysis approach when some data
are missing. You provide the available data and the assumed
causal structure, including the likely causes of missing data.
'midoc' will advise whether multiple imputation is needed, and
if so, how best to perform it.https://github.com/r-universe/mrcieu/actions/runs/10315989457Fri, 09 Aug 2024 08:03:05 GMTmidoc0.0.0.9000successhttps://mrcieu.r-universe.devhttps://github.com/elliecurnow/midocmidoc.rmdmidoc.htmlMultiple Imputation DOCtor (midoc)2024-02-26 14:46:312024-02-28 08:22:59midocdemoShiny.rmdmidocdemoShiny.htmlMultiple Imputation DOCtor (midoc) Shiny version2023-08-11 13:54:382024-02-28 08:22:59[mrcieu] ColliderBias 1.0.1Frank Dudbridge <frank.dudbridge@leicester.ac.uk>, Siyang
Cai <sc814@leicester.ac.uk>This package provides adjustment for collider bias and
weak instrument bias in association statistics in the context
of a genome-wide association study for a subsequent event,
using CWLS (Corrected Weighted Least Squares), MR-RAPS and
Slope Hunter. An estimation of true causal effect between
disease progression and an exposure of interest can be computed
using generalised instrument effect regression with CWBLS
(Corrected Weighted Bivariate Least Squares).https://github.com/r-universe/mrcieu/actions/runs/10301920303Thu, 08 Aug 2024 11:42:02 GMTColliderBias1.0.1successhttps://mrcieu.r-universe.devhttps://github.com/SiyangCai/ColliderBias[mrcieu] causl 0.9.2.9000evans@stats.ox.ac.uk (Robin Evans)Model multivariate distributions using causal parameters.https://github.com/r-universe/mrcieu/actions/runs/10263719469Tue, 06 Aug 2024 08:55:41 GMTcausl0.9.2.9000failurehttps://mrcieu.r-universe.devhttps://github.com/rje42/causlComparison.RmdComparison.htmlComparison of Methods2021-05-31 17:12:542023-11-28 15:39:06CopulaSimulation.RmdCopulaSimulation.htmlCopula Simulation2021-05-31 17:12:542021-10-05 15:53:59Discrete_Variables_Copula.RmdDiscrete_Variables_Copula.htmlDiscrete Variables Copula2023-10-03 09:15:292024-08-06 08:55:41Hidden_Variables.RmdHidden_Variables.htmlHidden Variables2021-05-31 17:12:542023-10-03 09:15:29Inversion.RmdInversion.htmlInversion Tutorial2023-10-03 09:15:292024-08-06 08:55:41[mrcieu] alspac 0.48.1g.hemani@bristol.ac.uk (Gibran Hemani)Functions to search and extract variables based on
keywords from the ALSPAC data dictionary.https://github.com/r-universe/mrcieu/actions/runs/10208002600Thu, 01 Aug 2024 23:44:49 GMTalspac0.48.1successhttps://mrcieu.r-universe.devhttps://github.com/explodecomputer/alspac[mrcieu] genepi.utils 0.0.18nicholas.sunderland@bristol.ac.uk (Nicholas Sunderland)The genepi.utils package is a collection of utility
functions for working with genetic epidemiology data.https://github.com/r-universe/mrcieu/actions/runs/10092280800Thu, 25 Jul 2024 10:02:53 GMTgenepi.utils0.0.18failurehttps://mrcieu.r-universe.devhttps://github.com/nicksunderland/genepi.utils[mrcieu] GenomicSEM 0.0.5agrotzin@utexas.edu (Andrew Grotzinger)Laterhttps://github.com/r-universe/mrcieu/actions/runs/10314422641Wed, 10 Jul 2024 17:02:04 GMTGenomicSEM0.0.5successhttps://mrcieu.r-universe.devhttps://github.com/GenomicSEM/GenomicSEM[mrcieu] TwoSampleMR 0.6.6g.hemani@bristol.ac.uk (Gibran Hemani)A package for performing Mendelian randomization using
GWAS summary data. It uses the IEU GWAS database
<https://gwas.mrcieu.ac.uk/> to automatically obtain data, and
a wide range of methods to run the analysis. You can use the
MR-Base web app <https://www.mrbase.org/> to try out a limited
range of the functionality in this package, but for any serious
work we strongly recommend using this R package.https://github.com/r-universe/mrcieu/actions/runs/10260810517Sun, 07 Jul 2024 18:14:50 GMTTwoSampleMR0.6.6successhttps://mrcieu.r-universe.devhttps://github.com/MRCIEU/TwoSampleMRexposure.Rmdexposure.htmlExposure data2020-01-11 20:36:352024-03-21 11:36:51harmonise.Rmdharmonise.htmlHarmonise data2020-01-11 20:36:352024-03-21 11:36:51introduction.Rmdintroduction.htmlIntroduction2020-01-11 20:36:352024-04-22 10:43:11gwas2020.Rmdgwas2020.htmlMajor changes to the IEU GWAS resources for 20202020-01-11 20:36:352024-02-01 11:10:20outcome.Rmdoutcome.htmlOutcome data2020-01-11 20:36:352024-03-21 11:36:51perform_mr.Rmdperform_mr.htmlPerform MR2020-01-11 20:36:352024-03-21 11:36:51[mrcieu] geni.plots 0.1.1jrstaley95@gmail.com (James Staley)GENI plots is designed to visualise results from
genome-wide association studies.https://github.com/r-universe/mrcieu/actions/runs/10225774007Thu, 04 Jul 2024 17:56:50 GMTgeni.plots0.1.1successhttps://mrcieu.r-universe.devhttps://github.com/jrs95/geni.plotsgeni_plots.Rmdgeni_plots.htmlGENI plots2023-11-14 14:17:372024-04-05 11:01:20[mrcieu] ieugwasr 1.0.1g.hemani@bristol.ac.uk (Gibran Hemani)Interface to the 'OpenGWAS' database API
<https://gwas-api.mrcieu.ac.uk/>. Includes a wrapper to make
generic calls to the API, plus convenience functions for
specific queries.https://github.com/r-universe/mrcieu/actions/runs/10121325079Thu, 27 Jun 2024 19:41:44 GMTieugwasr1.0.1successhttps://mrcieu.r-universe.devhttps://github.com/MRCIEU/ieugwasrguide.Rmdguide.htmlPerform fast queries against a massive database of complete GWAS summary data2019-11-06 00:47:492024-04-21 21:22:32local_ld.Rmdlocal_ld.htmlRunning local LD operations2020-06-02 15:06:132024-03-12 20:50:46[mrcieu] cit 2.4.0joshua.millstein@usc.edu (Joshua Millstein)A likelihood-based hypothesis testing approach is
implemented for assessing causal mediation. Described in
Millstein, Chen, and Breton (2016),
<DOI:10.1093/bioinformatics/btw135>, it could be used to test
for mediation of a known causal association between a DNA
variant, the 'instrumental variable', and a clinical outcome or
phenotype by gene expression or DNA methylation, the potential
mediator. Another example would be testing mediation of the
effect of a drug on a clinical outcome by the molecular target.
The hypothesis test generates a p-value or permutation-based
FDR value with confidence intervals to quantify uncertainty in
the causal inference. The outcome can be represented by either
a continuous or binary variable, the potential mediator is
continuous, and the instrumental variable can be continuous or
binary and is not limited to a single variable but may be a
design matrix representing multiple variables.https://github.com/r-universe/mrcieu/actions/runs/10121169481Thu, 27 Jun 2024 04:50:38 GMTcit2.4.0successhttps://mrcieu.r-universe.devhttps://github.com/USCbiostats/cit[mrcieu] OneSampleMR 0.1.5remlapmot@hotmail.com (Tom Palmer)Useful functions for one-sample (individual level data)
Mendelian randomization and instrumental variable analyses. The
package includes implementations of; the Sanderson and
Windmeijer (2016) <doi:10.1016/j.jeconom.2015.06.004>
conditional F-statistic, the multiplicative structural mean
model HernĂ¡n and Robins (2006)
<doi:10.1097/01.ede.0000222409.00878.37>, and two-stage
predictor substitution and two-stage residual inclusion
estimators explained by Terza et al. (2008)
<doi:10.1016/j.jhealeco.2007.09.009>.https://github.com/r-universe/mrcieu/actions/runs/10106460461Wed, 26 Jun 2024 18:27:16 GMTOneSampleMR0.1.5successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/OneSampleMRcompare-smm-fits.Rmdcompare-smm-fits.htmlComparison fits of the multiplicative structural mean model2021-08-19 14:38:082023-05-12 16:41:23f-statistic-comparison.Rmdf-statistic-comparison.htmlComparison of conditional F-statistics2021-08-19 14:38:082023-05-12 16:42:36[mrcieu] MrDAG 0.1.0lb664@cam.ac.uk (Leonardo Bottolo)This package performs Mendelian randomization for multiple
exposures and outcomes with Bayesian Directed Acyclic Graphs
exploration and causal effects estimation.https://github.com/r-universe/mrcieu/actions/runs/10071023410Mon, 24 Jun 2024 08:39:01 GMTMrDAG0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/lb664/MrDAG[mrcieu] MRAPSS 0.0.0.9000maxhu@ust.hk (Xianghong HU)The MRAPSS package implement the MR-APPSS approach to test
for the causal effects between an exposure and a outcome
disease. The MR-APPSS is a unified approach to Mendelian
Randomization accounting for polygenicity, pleiotropy and
sample structure using genome-wide summary statistics.
Specifically, MR-APPSS uses a background-foreground model to
characterize both SNP-exposure effects and SNP-outcome effects
estimates, where the background model accounts for confounding
from genetic correlation and sample structure and the
foreground model captures the valid signal for causal
inference.https://github.com/r-universe/mrcieu/actions/runs/10035066807Sat, 22 Jun 2024 12:34:46 GMTMRAPSS0.0.0.9000successhttps://mrcieu.r-universe.devhttps://github.com/YangLabHKUST/MR-APSS[mrcieu] bpbounds 0.1.6remlapmot@hotmail.com (Tom Palmer)Implementation of the nonparametric bounds for the average
causal effect under an instrumental variable model by Balke and
Pearl (Bounds on Treatment Effects from Studies with Imperfect
Compliance, JASA, 1997, 92, 439, 1171-1176,
<doi:10.2307/2965583>). The package can calculate bounds for a
binary outcome, a binary treatment/phenotype, and an instrument
with either 2 or 3 categories. The package implements bounds
for situations where these 3 variables are measured in the same
dataset (trivariate data) or where the outcome and instrument
are measured in one study and the treatment/phenotype and
instrument are measured in another study (bivariate data).https://github.com/r-universe/mrcieu/actions/runs/9917647289Thu, 13 Jun 2024 15:00:08 GMTbpbounds0.1.6successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/bpboundsbpbounds.Rmdbpbounds.htmlNonparametric bounds for the average causal effect: bpbounds examples2018-10-25 21:57:232023-10-10 13:10:18[mrcieu] BWMR 0.1.1yourself@somewhere.net (The package maintainer)Inference the causality based on BWMR method.https://github.com/r-universe/mrcieu/actions/runs/9903181751Wed, 12 Jun 2024 10:21:42 GMTBWMR0.1.1successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/BWMRBWMR_package.RnwBWMR_package.pdfBWMR Package for causal inference based on summary statistics2019-07-21 03:38:252024-06-12 10:10:05[mrcieu] SUMnlmr 0.0.0.9000am2609@medschl.cam.ac.uk (Amy Mason)Runs non-linear MR calculations on partly-summarized data.https://github.com/r-universe/mrcieu/actions/runs/10129278342Wed, 29 May 2024 10:22:31 GMTSUMnlmr0.0.0.9000successhttps://mrcieu.r-universe.devhttps://github.com/amymariemason/SUMnlmr[mrcieu] TVMR 0.1.0haodong.tian@mrc-bsu.cam.ac.uk (Haodong Tian)Use functional principal components analysis (FPCA) within
multivariable Mendelian randomization (MVMR) to estimate the
time-varying effect function.https://github.com/r-universe/mrcieu/actions/runs/10017935325Sat, 11 May 2024 07:01:42 GMTTVMR0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/HDTian/TVMR[mrcieu] RFQT 0.1.0haodong.tian@mrc-bsu.cam.ac.uk (Haodong Tian)Data-adaptive method for effect heterogeneity analysis or
non-linear causal studies in Mendelian randomization and
instrumental variables analysis.https://github.com/r-universe/mrcieu/actions/runs/10129354665Sat, 11 May 2024 06:59:51 GMTRFQT0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/HDTian/RFQT[mrcieu] DRMR 0.1.0haodong.tian@mrc-bsu.cam.ac.uk (Haodong Tian)Doubly-ranked and residual stratification for instrumental
variable and Mendelian randomization studies and further
stratification-based analysis.https://github.com/r-universe/mrcieu/actions/runs/10017935327Sat, 11 May 2024 06:43:21 GMTDRMR0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/HDTian/DRMR[mrcieu] MRBEE 0.1.0noahlorinczcomi@gmail.com (Noah Lorincz-Comi)This package performs multivariate Mendelian
randomization. It is characterized by the removal of
measurement error bias caused by the estimation error of GWAS
effect size estimates using an unbiased estimating function in
measurement error analysis. It also utilizes a pleiotropy test
to dynamically detect and remove potential pleiotropy, making
the causal effect robust to pleiotropy.https://github.com/r-universe/mrcieu/actions/runs/10088657845Wed, 01 May 2024 16:49:29 GMTMRBEE0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/noahlorinczcomi/MRBEE[mrcieu] mr.pivw 0.1.3sqxu@hku.hk (Siqi Xu)The penalized inverse-variance weighted (pIVW) estimator
is a Mendelian randomization method for estimating the causal
effect of an exposure variable on an outcome of interest based
on summary-level GWAS data. The pIVW estimator accounts for
weak instruments and balanced horizontal pleiotropy
simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022)
<doi:10.1111/biom.13732>.https://github.com/r-universe/mrcieu/actions/runs/10109621079Sun, 28 Apr 2024 03:38:17 GMTmr.pivw0.1.3successhttps://mrcieu.r-universe.devhttps://github.com/siqixu/mr.pivw[mrcieu] gsmr2 1.1.1Zhihong Zhu <z.zhu1@uq.edu.au>, Angli Xue
<a.xue@garvan.org.au>, Jian Yang <jian.yang@westlake.edu.cn>GSMR2 (Generalised Summary-data-based Mendelian
Randomisation v2) is an improved version of GSMR, which uses
GWAS summary statistics to test for a putative causal
association between two phenotypes (e.g., a modifiable risk
factor and a disease) based on a multi-SNP model. This version
implements a global heterogeneity test to remove invalid
instrumental variables and provides a causal estimation that is
more robust to directional pleiotropy.https://github.com/r-universe/mrcieu/actions/runs/9886104386Fri, 26 Apr 2024 01:50:23 GMTgsmr21.1.1failurehttps://mrcieu.r-universe.devhttps://github.com/jianyanglab/gsmr2GSMR-intro.RmdGSMR-intro.htmlAn Introduction to gsmr2023-09-13 06:14:122023-09-13 06:14:12[mrcieu] MRZero 0.2.0sb452@medschl.cam.ac.uk (Stephen Burgess)Encodes several methods for performing Mendelian
randomization analyses with summarized data. Similar to the
'MendelianRandomization' package, but with fewer bells and
whistles, and less frequent updates. As described in Yavorska
(2017) <doi:10.1093/ije/dyx034> and Broadbent (2020)
<doi:10.12688/wellcomeopenres.16374.2>.https://github.com/r-universe/mrcieu/actions/runs/9886104362Mon, 15 Apr 2024 04:09:14 GMTMRZero0.2.0successhttps://mrcieu.r-universe.devhttps://github.com/cran/MRZero[mrcieu] MendelianRandomization 0.10.0sb452@medschl.cam.ac.uk (Stephen Burgess)Encodes several methods for performing Mendelian
randomization analyses with summarized data. Summarized data on
genetic associations with the exposure and with the outcome can
be obtained from large consortia. These data can be used for
obtaining causal estimates using instrumental variable methods.https://github.com/r-universe/mrcieu/actions/runs/10006741104Sat, 13 Apr 2024 02:27:20 GMTMendelianRandomization0.10.0successhttps://mrcieu.r-universe.devhttps://github.com/cran/MendelianRandomizationVignette_MR.RmdVignette_MR.htmlMendelian randomization vignette2016-08-31 12:15:382024-04-13 02:27:20[mrcieu] GWASBrewer 0.3.0.0199jvmorr@umich.edu (Jean Morrison)Simulate GWAS summary statistics from specified DAG or
factor structure.https://github.com/r-universe/mrcieu/actions/runs/10243012314Sun, 07 Apr 2024 17:26:13 GMTGWASBrewer0.3.0.0199successhttps://mrcieu.r-universe.devhttps://github.com/jean997/GWASBrewereffect_distribution.Rmdeffect_distribution.htmlControlling Effect Size Distributions2023-04-17 16:20:392024-03-27 21:32:26resampling.Rmdresampling.htmlResampling and Re-Scaling Summary and Individual Level Data2023-08-23 22:42:322024-03-27 21:32:26simulating_data.Rmdsimulating_data.htmlSimulating Data2022-08-24 22:12:062024-04-07 17:26:13[mrcieu] MVMRcML 0.1.0zl23k@fsu.edu (Zhaotong Lin)Robust multivariable Mendelian randomization based on
constrained maximum likelihood.https://github.com/r-universe/mrcieu/actions/runs/10224641275Fri, 05 Apr 2024 01:41:28 GMTMVMRcML0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/ZhaotongL/MVMR-cML[mrcieu] CAMeRa 0.1.0g.hemani@bristol.ac.uk (Gibran Hemani)CAMERA estimates joint causal effect in multiple
ancestries and detects pleiotropy via the zero relevance model.https://github.com/r-universe/mrcieu/actions/runs/10209853637Thu, 04 Apr 2024 07:53:42 GMTCAMeRa0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/MRCIEU/CAMERAimport-local.Rmdimport-local.htmlImport to CAMERA from local data2024-03-24 07:56:422024-03-24 22:37:20tutorial.Rmdtutorial.htmlTutorial2024-03-24 07:56:422024-03-24 22:37:20[mrcieu] GRAPPLE 0.2.2jingshuw@uchicago.edu (Jingshu Wang)Fitting and diagnosing two-sample summary data Mendelian
randomization with heterogeneous instruments.https://github.com/r-universe/mrcieu/actions/runs/10088125985Wed, 27 Mar 2024 21:30:09 GMTGRAPPLE0.2.2successhttps://mrcieu.r-universe.devhttps://github.com/jingshuw/GRAPPLE[mrcieu] prop.coloc 1.1.0ashish.patel@mrc-bsu.cam.ac.uk (Ashish Patel)A proportional colocalization test that accounts for
uncertainty in variant selection using summary data.https://github.com/r-universe/mrcieu/actions/runs/10017935662Tue, 26 Mar 2024 17:22:55 GMTprop.coloc1.1.0successhttps://mrcieu.r-universe.devhttps://github.com/ash-res/prop-colocprop-coloc-vignette.Rmdprop-coloc-vignette.htmlprop-coloc-vignette2024-02-01 23:23:492024-03-26 17:07:22[mrcieu] lhcMR 0.0.0.9000liza.darrous@unil.ch (Liza Darrous)lhcMR esimates a causal effect between two traits while
accounting for a possible latent heritable confounder acting on
them, as well as sample overlap.https://github.com/r-universe/mrcieu/actions/runs/10051622028Sun, 24 Mar 2024 12:22:03 GMTlhcMR0.0.0.9000successhttps://mrcieu.r-universe.devhttps://github.com/LizaDarrous/lhcMR[mrcieu] simmrd 0.0.0.9000noahlorinczcomi@gmail.com (Noah Lorincz-Comi)This package generates simulation data to use in the
evaluation of univariable or multivariable Mendelian
Randomization methods. MR scenarios can include uncorrelated
horizontal pleiotropy, correlated horizontal pleiotropy, weak
instruments, winner's curse, and correlated SNP instruments.https://github.com/r-universe/mrcieu/actions/runs/9985737887Wed, 20 Mar 2024 16:42:45 GMTsimmrd0.0.0.9000successhttps://mrcieu.r-universe.devhttps://github.com/noahlorinczcomi/simmrdtutorial.Rmdtutorial.htmlTutorial2024-01-05 17:25:192024-01-05 17:25:19[mrcieu] iGasso 1.6.1kai-wang@uiowa.edu (Kai Wang)A collection of statistical tests for genetic association
studies and summary data based Mendelian randomization.https://github.com/r-universe/mrcieu/actions/runs/9886104375Tue, 12 Mar 2024 02:31:09 GMTiGasso1.6.1successhttps://mrcieu.r-universe.devhttps://github.com/cran/iGasso[mrcieu] winnerscurse 0.1.1a.forde21@universityofgalway.ie (Amanda Forde)Designed to provide users with easy access to published
methods which aim to correct for Winner's Curse using only
summary statistics from genome-wide association studies. With
merely estimates of effect size and associated standard error
for each genetic variant, users are able to implement these
methods to obtain more accurate estimates of the true effect
sizes. These methods can be applied to data from both
quantitative and binary traits.https://github.com/r-universe/mrcieu/actions/runs/10233823083Thu, 07 Mar 2024 12:08:54 GMTwinnerscurse0.1.1successhttps://mrcieu.r-universe.devhttps://github.com/amandaforde/winnerscursediscovery_replication.Rmddiscovery_replication.htmlMethods for use with discovery and replication GWASs2021-03-18 13:35:512023-12-06 11:44:02winners_curse_methods.Rmdwinners_curse_methods.htmlMethods for use with discovery GWAS2021-02-03 16:51:192023-12-06 11:44:02standard_errors_confidence_intervals.Rmdstandard_errors_confidence_intervals.htmlStandard errors and confidence intervals of adjusted estimates2021-03-09 11:44:502023-12-06 11:44:02[mrcieu] metaboprep 1.0.1hughes.evoanth@gmail.com (David Hughes)Reads in raw Metabolon and Nightingale xls sheets and aids
in data preparation of all metabolomics data sets.https://github.com/r-universe/mrcieu/actions/runs/10225431369Wed, 06 Mar 2024 12:22:49 GMTmetaboprep1.0.1successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/metaboprep[mrcieu] MRPATH 1.0daniong@umich.edu (Daniel)This package implements methods for fitting the MR-PATH
model.https://github.com/r-universe/mrcieu/actions/runs/10209853846Tue, 05 Mar 2024 14:54:52 GMTMRPATH1.0successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/MRPATH[mrcieu] MrMediation 1.0zixuanwu@uchicago.edu (Zixuan Wu)A Bayesian framework for Mendelian Randomization in the
mediation setting.https://github.com/r-universe/mrcieu/actions/runs/10209723724Tue, 05 Mar 2024 12:26:41 GMTMrMediation1.0successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/MrMediation[mrcieu] mrcovreg 1.0.0andrew.grant@mrc-bsu.cam.ac.uk (Andrew Grant)Implement a method for an efficient and robust approach to
Mendelian randomization with measured pleiotropic effects in a
high-dimensional setting.https://github.com/r-universe/mrcieu/actions/runs/10209483531Tue, 05 Mar 2024 10:24:16 GMTmrcovreg1.0.0successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/mrcovreg[mrcieu] MR.RGM 0.0.2bitan@tamu.edu (Bitan Sarkar)Addressing a central challenge encountered in Mendelian
randomization (MR) studies, where MR primarily focuses on
discerning the effects of individual exposures on specific
outcomes and establishes causal links between them. Using a
network-based methodology, the intricacy involving
interdependent outcomes due to numerous factors has been
tackled through this routine. Based on Ni et al. (2018)
<doi:10.1214/17-BA1087>, 'MR.RGM' extends to a broader
exploration of the causal landscape by leveraging on network
structures and involves the construction of causal graphs that
capture interactions between response variables and
consequently between responses and instrument variables.
'MR.RGM' facilitates the navigation of various data
availability scenarios effectively by accommodating three input
formats, i.e., individual-level data and two types of
summary-level data. In the process, causal effects, adjacency
matrices, and other essential parameters of the complex
biological networks, are estimated. Besides, 'MR.RGM' provides
uncertainty quantification for specific network structures
among response variables.https://github.com/r-universe/mrcieu/actions/runs/9886104293Fri, 01 Mar 2024 20:33:34 GMTMR.RGM0.0.2successhttps://mrcieu.r-universe.devhttps://github.com/bitansa/MR.RGM[mrcieu] finemapr 0.1.0andrey@example.com (Andrey Ziyatdinov)R wrapper to fine-mappers.https://github.com/r-universe/mrcieu/actions/runs/10209624164Tue, 16 Jan 2024 15:09:59 GMTfinemapr0.1.0failurehttps://mrcieu.r-universe.devhttps://github.com/remlapmot/finemapr[mrcieu] MRMiSTERI 0.1.0zhhliu@hku.hk (Zhonghua Liu)This package performs robust Mendelian randomization to
estimate the effect of treatment on the treated with possibly
invalid IVs.https://github.com/r-universe/mrcieu/actions/runs/10025531911Tue, 16 Jan 2024 14:52:22 GMTMRMiSTERI0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/remlapmot/MRMiSTERI[mrcieu] mr.simss 0.1.0a.forde21@universityofgalway.ie (Amanda Forde)Designed to provide users with a method, namely MR-SimSS,
which uses simulated sample splitting in order to alleviate
Winner's Curse bias in MR causal effect estimates. This
approach also takes into account sample overlap between the
exposure and outcome genome-wide association studies. It uses
summary statistics from genome-wide association studies and
works in combination with existing MR methods, such as IVW and
MR-RAPS.https://github.com/r-universe/mrcieu/actions/runs/10006542866Mon, 15 Jan 2024 10:25:41 GMTmr.simss0.1.0successhttps://mrcieu.r-universe.devhttps://github.com/amandaforde/mr.simssderive-MR-SimSS.Rmdderive-MR-SimSS.htmlDeriving MR-SimSS2023-11-07 11:16:412023-11-07 11:16:41perform-MR-SimSS.Rmdperform-MR-SimSS.htmlPerforming MR-SimSS2023-11-06 15:20:262023-11-07 22:19:27[mrcieu] jlst 0.0.2jrstaley95@gmail.com (James Staley)Joint location-and-scale tests for joint testing of mean
(location) and variance (scale).https://github.com/r-universe/mrcieu/actions/runs/10209624046Thu, 04 Jan 2024 21:27:52 GMTjlst0.0.2successhttps://mrcieu.r-universe.devhttps://github.com/jrs95/jlst[mrcieu] lmrse 0.0.7jrstaley95@gmail.com (James Staley)Longtiudinal analysis of high-dimensional data using
linear regression with clustered robust standard errors across
markers.https://github.com/r-universe/mrcieu/actions/runs/10209624083Thu, 04 Jan 2024 20:15:21 GMTlmrse0.0.7successhttps://mrcieu.r-universe.devhttps://github.com/jrs95/lmrse[mrcieu] nlmr 1.0.3jrstaley95@gmail.com (James Staley)Non-linear Mendelian randomization analysis to investigate
the shape of exposure-outcome relationships.https://github.com/r-universe/mrcieu/actions/runs/10191526130Thu, 04 Jan 2024 20:10:00 GMTnlmr1.0.3successhttps://mrcieu.r-universe.devhttps://github.com/jrs95/nlmr[mrcieu] gwasglue2 0.0.0.9000rita.rasteiro@bristol.ac.uk (Rita Rasteiro)Description: Many tools exist that use GWAS summary data
for colocalisation, fine mapping, Mendelian randomization,
visualisation, etc. This package is a conduit that connects R
packages that can retrieve GWAS summary data to various tools
for analysing those data.https://github.com/r-universe/mrcieu/actions/runs/10087638669Tue, 28 Nov 2023 13:12:30 GMTgwasglue20.0.0.9000successhttps://mrcieu.r-universe.devhttps://github.com/MRCIEU/gwasglue2Strategy.RmdStrategy.htmlStrategy2022-11-23 15:15:402023-07-17 16:09:44SummarySet_DataSet.RmdSummarySet_DataSet.htmlTutorial 1: How to create a SummarySet and a DataSet2023-07-17 16:09:442023-09-12 16:41:56mr.Rmdmr.htmlTutorial 2: MR analysis2023-06-27 10:04:262023-07-17 16:09:44finemap_coloc1.Rmdfinemap_coloc1.htmlTutorial 3: Regional genotype-phenotype map2023-06-27 10:04:262023-07-17 16:09:44meta.Rmdmeta.htmlTutorial 4: Meta analysis2023-06-13 15:50:582023-08-03 19:43:02finemap_coloc2.Rmdfinemap_coloc2.htmlTutorial 5: Alternative regional genotype-phenotype map example, with meta-analyses included2023-06-27 10:04:262023-08-03 19:43:02liftover.Rmdliftover.htmlTutorial 6: Remap genomic coordinates to a different genome assembly2023-08-03 19:43:022023-08-08 14:25:19[mrcieu] MR.LDP 1.0your@email.com (Your Name)More details about what the package does. See
<http://cran.r-project.org/doc/manuals/r-release/R-exts.html#The-DESCRIPTION-file>
for details on how to write this part.https://github.com/r-universe/mrcieu/actions/runs/10260511852Mon, 27 Nov 2023 07:55:49 GMTMR.LDP1.0successhttps://mrcieu.r-universe.devhttps://github.com/QingCheng0218/MR.LDP[mrcieu] MR.CUE 1.0yourfault@somewhere.net (Who to complain to)More about what it does (maybe more than one line)https://github.com/r-universe/mrcieu/actions/runs/10278380128Mon, 27 Nov 2023 06:55:36 GMTMR.CUE1.0successhttps://mrcieu.r-universe.devhttps://github.com/QingCheng0218/MR.CUE[mrcieu] EpiViz 0.0.1yourself@somewhere.net (The package maintainer)An implementation of Circos plots for epidemiologists in
R. It takes the Circlize package and adapts it for use by
epidemiologists. Circos plots provide an informative way of
plotting greater than 50 plotting points. A legend can be
plotted automatically and customised.https://github.com/r-universe/mrcieu/actions/runs/9951380127Tue, 31 Oct 2023 09:34:26 GMTEpiViz0.0.1successhttps://mrcieu.r-universe.devhttps://github.com/mattlee821/EpiViz[mrcieu] gsmr 1.0.6z.zhu1@uq.edu.au (Zhihong Zhu)A tool perform Generalized Summary-data-based Mendelian
Randomization analysis (GSMR) and HEterogeneity In Dependent
Instruments analysis to remove pleiotropic outliers
(HEIDI-outlier)https://github.com/r-universe/mrcieu/actions/runs/9886104267Wed, 13 Sep 2023 06:30:02 GMTgsmr1.0.6failurehttps://mrcieu.r-universe.devhttps://github.com/jianyanglab/gsmrGSMR-intro.RmdGSMR-intro.htmlAn Introduction to gsmr2023-09-13 06:13:342023-09-13 06:13:34[mrcieu] genetics.binaRies 0.1.1g.hemani@bristol.ac.uk (Gibran Hemani)A convenient way to make binaries such as plink, bcftools,
and others available to other packages. This package forms part
of the MRC IEU OpenGWAS system. The system includes other R
packages, including; gwasglue, ieugwasr, gwasvcf, and
TwoSampleMR.https://github.com/r-universe/mrcieu/actions/runs/9951380639Wed, 06 Sep 2023 11:04:04 GMTgenetics.binaRies0.1.1successhttps://mrcieu.r-universe.devhttps://github.com/MRCIEU/genetics.binaRies