TwoSampleMR - Two Sample MR Functions and Interface to MR Base Database

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.

Last updated 2 months ago

11.13 score 421 stars 1 packages 1.7k scripts

ieugwasr - Interface to the 'OpenGWAS' Database API

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.

Last updated 4 months ago

10.51 score 73 stars 5 packages 318 scripts 3.9k downloads

TrialEmulation - Causal Analysis of Observational Time-to-Event Data

Implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

Last updated 2 months ago

causal-inferencelongitudinal-datasurvival-analysis

7.46 score 18 stars 24 scripts 416 downloads

Statamarkdown - 'Stata' Markdown

Settings and functions to extend the 'knitr' 'Stata' engine.

Last updated 11 months ago

6.75 score 59 stars 478 scripts 392 downloads

OneSampleMR - One Sample Mendelian Randomization and Instrumental Variable Analyses

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 Hernán 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>.

Last updated 2 months ago

instrumental-variableinstrumental-variablesmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomization

6.69 score 18 stars 16 scripts 259 downloads

OneSampleMR - One Sample Mendelian Randomization and Instrumental Variable Analyses

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 Hernán 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>.

Last updated 2 months ago

instrumental-variableinstrumental-variablesmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomization

6.69 score 18 stars 16 scripts 259 downloads

mrbayes - Bayesian Summary Data Models for Mendelian Randomization Studies

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

Last updated 2 months ago

5.08 score 4 stars 2 scripts 330 downloads

mrbayes - Bayesian Summary Data Models for Mendelian Randomization Studies

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

Last updated 2 months ago

5.08 score 4 stars 2 scripts 330 downloads

mrbayes - Bayesian Summary Data Models for Mendelian Randomization Studies

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

Last updated 2 months ago

5.08 score 4 stars 2 scripts 317 downloads

bpbounds - Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions

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).

Last updated 2 months ago

aceaverage-causal-effectboundsinstrumental-variableivmendelianmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomizationmrnonparametricnonparametric-boundspearlshiny

5.01 score 12 scripts 735 downloads

bpbounds - Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions

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).

Last updated 2 months ago

aceaverage-causal-effectboundsinstrumental-variableivmendelianmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomizationmrnonparametricnonparametric-boundspearlshiny

5.01 score 12 scripts 735 downloads

knitexercise - Knit Exercise Questions and Solutions from the Same R Markdown Document

Provides a function to knit questions and solutions documents from the same R Markdown document.

Last updated 2 months ago

exercisesknitrrmarkdown

3.78 score 1 stars 1 scripts

StepReg - Stepwise Regression Analysis

The stepwise regression analysis is a statistical technique used to identify a subset of predictor variables essential for constructing predictive models. This package performs stepwise regression analysis across various regression models such as linear, logistic, Cox proportional hazards, Poisson, and gamma regression. It incorporates diverse stepwise regression algorithms like forward selection, backward elimination, and bidirectional elimination alongside the best subset method. Additionally, it offers a wide range of selection criteria, including Akaike Information Criterion (AIC), corrected AIC (AICc), Sawa Bayesian Information Criterion (BIC), Schwarz Bayesian Information Criterion (SBC), Significant Levels (SL), among others. Moreover, it facilitates the concurrent selection of multiple methods and criteria for variable selection. For user-friendly exploration and analysis, StepReg provides an intuitive R Shiny app.

Last updated 7 months ago

3.53 score 29 scripts 1.2k downloads

tmsens - Sensitivity Analysis Using the Trimmed Means Estimator

Sensitivity analysis using the trimmed means estimator.

Last updated 2 months ago

missing-datasensitivity-analysistrimmed-means

2.70 score 1 stars 1 scripts 171 downloads

dd4d - Dummy Data for Dummies

Allows you to specify and sample from a Bayesian Network (a.k.a. a parametric Directed Acyclic Graph, or pDAG).

Last updated 1 years ago

2.00 score 2 stars 7 scripts

osutils - Useful Functions for OpenSAFELY

Contains functions that are often needed when using the OpenSAFELY platform <https://www.opensafely.org/>, such as redaction and low-memory processing.

Last updated 2 years ago

1.70 score 1 scripts