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MendelianRandomization (version 0.9.0)

Mendelian Randomization Package

Description

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.

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Version

Install

install.packages('MendelianRandomization')

Monthly Downloads

2,728

Version

0.9.0

License

GPL-2 | GPL-3

Maintainer

Stephen Burgess

Last Published

August 8th, 2023

Functions in MendelianRandomization (0.9.0)

MVLasso-class

MRMVLasso class
MVGMM-class

MVGMM Class
MVMRcML-class

MVMRcML Class
MRMVInput-class

MRMVInput Class
MVPCGMM-class

MVPCGMM Class
MRcML-class

MRcML Class
MVIVW-class

MVIVW Class
MVEgger-class

MVEgger Class
MVMedian-class

MRMVMedian class
cML_SdTheta

Standard Error of Estimated Theta
MVcML_SdTheta

Standard error estimate for MVMR-cML-BIC
MaxLik-class

MaxLik Class
cML_estimate_random

Estimate with Regular Likelihood Using Multiple Random Start Points
calcium

Data on effect of calcium on fasting glucose (correlated variants)
PIVW-class

PIVW Class
MVmr_cML

MVMRcML method with Data Perturbation
PCGMM-class

PCGMM Class
cML_estimate

Estimate with Regular Likelihood
invcov_mvmr

Generate the list of inverse of covariance matrices used in MVMR-cML-DP
het.weight

Heterogeneity-penalized weight function
WeightedMedian-class

WeightedMedian Class
extract.pheno.csv

Extract summarized data from a PhenoScanner .csv file (legacy)
model.prior

Prior weight function
MVmr_cML_DP

MVMRcML method with Data Perturbation
loglikelihoodcorrel

Calculates log-likelihood with correlated variants in two-sample setting (no correlation from sample overlap)
ci_normal

Calculate confidence intervals using the normal distribution
ci_t

Calculate confidence intervals using the t-distribution
decimals

Produce nicely rounded numbers
loglikelihoodrhocorrel

Calculates log-likelihood with correlation from sample overlap
mr_allmethods

Mendelian randomization estimation using all methods
getter

Applies method $ to different classes
mbe_boot

Mode-based estimate (Hartwig) bootstrap function
mbe_est

Mode-based estimate (Hartwig) estimation function
mr_conmix

Contamination mixture method
mr_cML

Constrained maximum likelihood (cML) method
coursedata

Course data
condFstat

Calculates conditional F-statistic for each risk factor using summarized data
egger.bounds

Calculates confidence intervals for the MR-Egger method
loglikelihood

Calculates log-likelihood with uncorrelated variants in two-sample setting (no correlation from sample overlap)
ldlc

Data on lipid effects on coronary artery disease (uncorrelated variants)
mr_divw

Debiased inverse-variance weighted method
mr_funnel

Draw a funnel plot of variant-specific estimates
mr_ivw

Inverse-variance weighted method
mr_egger

MR-Egger method
mr_forest

Draw a forest plot of causal estimates
mr_hetpen

Heterogeneity-penalized method
mr_input

Inputting and formatting data for use in causal estimation
mr_lasso

MR-Lasso method
mr_loo

Leave-one-out estimates
mr_maxlik

Maximum-likelihood method
mr_mvmedian

Multivariable median-based method
mr_mvegger

Multivariable MR-Egger method
mr_median

Median-based method
mr_mvpcgmm

Multivariable principal components generalized method of moments (PC-GMM) method
mr_mvinput

Inputting and formatting data for use in causal estimation
mr_mvivw

Multivariable inverse-variance weighted method
mr_mvgmm

Multivariable generalized method of moments (GMM) method
mr_mvcML

Multivariable constrained maximum likelihood method
mr_mbe

Mode-based method of Hartwig
mr_mvlasso

Multivariable MR-Lasso method
mr_plot

Draw a scatter plot of the genetic associations and/or causal estimates
phenoscanner

PhenoScanner
pheno_input

Extract summarized data from PhenoScanner
mr_pivw

Penalized inverse-variance weighted method
mr_pcgmm

Univariable principal components generalized method of moments (PC-GMM) method
penalised.weights.delta

Calculates p-values for penalization of weights with second-order weights
simpleCap

Capitalize a word
values

Applies method values() to different output classes
penalised.weights

Calculates p-values for penalization of weights
r.weights.delta

Calculates p-values for penalization of weights with second-order weights
pl

Profile likelihood of valid IVs
r.weights

Calculates p-values for penalization of weights
weighted.median

Weighted median function
weighted.median.boot.se

Weighted median standard error function
MRLasso-class

MRLasso class
MRConMix-class

MRConMix Class
MRAll-class

MRAll Class
MRHetPen-class

MRHetPen Class
IVW-class

IVW Class
Egger-class

Egger Class
MRMBE-class

MRMBE Class
MRInput-class

MRInput Class
DIVW-class

DIVW Class
BF_dist

Generate bootstrap samples for the bootstrapping Fieller's confidence interval of the penalized inverse-variance weighted (pIVW) method