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TwoSampleMR - Two Sample MR Functions and Interface to MRC Integrative Epidemiology Unit OpenGWAS Database

A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database <https://opengwas.io> to automatically obtain data, and a wide range of methods to run the analysis.

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11.83 score 548 stars 2 dependents 2.2k scripts

httpgd - A 'HTTP' Server Graphics Device

A graphics device for R that is accessible via network protocols. This package was created to make it easier to embed live R graphics in integrated development environments and other applications. The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics. The device asynchronously serves graphics via 'HTTP' and 'WebSockets'.

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cpp

10.96 score 505 stars 690 scripts 7.2k downloads

ieugwasr - Interface to the 'OpenGWAS' Database API

Interface to the 'OpenGWAS' database API <https://api.opengwas.io/api/>. Includes a wrapper to make generic calls to the API, plus convenience functions for specific queries.

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10.71 score 110 stars 7 dependents 726 scripts 3.8k 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>.

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causal-inferencelongitudinal-datasurvival-analysiscpp

8.24 score 38 stars 87 scripts 636 downloads

MVMR - MVMR

An R package for performing multivariable Mendelian randomization analyses.

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8.12 score 54 stars 1 dependents 210 scripts

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>.

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instrumental-variableinstrumental-variablesmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomization

7.31 score 25 stars 34 scripts 239 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

instrumental-variableinstrumental-variablesmendelian-randomisationmendelian-randomizationmendelianrandomisationmendelianrandomization

7.31 score 25 stars 34 scripts 239 downloads

SEQTaRget - Sequential Trial Emulation

Implementation of sequential trial emulation for the analysis of observational databases. The 'SEQTaRget' software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. 'SEQTaRget' allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up. (Paper to come).

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as-treatedbiostatisticscausal-inferenceepidemiologyinitiatorsintention-to-treatper-protocol

7.09 score 7 stars 22 scripts 584 downloads

colorout - Colorize R Output on Terminal Emulators

Colorize R output when it is running on a terminal emulator. The functions of this package only work if R is compiled for Unix systems and it is running interactively in a terminal emulator. The terminal must support Select Graphic Rendition (SGR, also known as ANSI escape codes or sequences). The package contains a C library with a function that replaces the default Rstd_WriteConsoleEx which, when enabled, is responsible for printing messages in the Console when R is running in interactive mode. It works with RStudio, but there is no support for Graphical User Interfaces that does not support ANSI colors, such as Windows RGui.

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7.08 score 304 stars 439 scripts 8 downloads

vscDebugger - Support for Visual Studio Code Debugger

Provides support for a visual studio code debugger

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debuggervscode

7.08 score 105 stars 57 scripts

Statamarkdown - 'Stata' Markdown

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

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6.78 score 60 stars 500 scripts 386 downloads

parglm - Parallel GLM

Provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.

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openblascpp

6.56 score 2 dependents 50 scripts 1.2k downloads

RadialMR - RadialMR

A package for implementing radial inverse variance weighted and MR-Egger methods.

Last updated

6.53 score 35 stars 4 dependents 147 scripts

RadialMR - RadialMR

A package for implementing radial inverse variance weighted and MR-Egger methods.

Last updated

6.53 score 35 stars 4 dependents 147 scripts

MRPRESSO - Performs the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test.

MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a framework that allows for the evaluation of pleiotropy in multi-instrument Mendelian Randomization utilizing genome-wide summary association statistics.

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6.27 score 95 stars 3 dependents 438 scripts

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.

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cpp

5.72 score 4 stars 9 scripts 277 downloads

mr.raps - Two Sample Mendelian Randomization using Robust Adjusted Profile Score

Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Gibran Hemani, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>; Qingyuan Zhao, Yang Chen, Jingshu Wang, Dylan S. Small. Powerful genome-wide design and robust statistical inference in two-sample summary-data Mendelian randomization. <arXiv:1804.07371>.

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5.37 score 42 stars 1 dependents 185 scripts 57 downloads

SASmarkdown - 'SAS' Markdown

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

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5.30 score 21 stars 95 scripts 421 downloads

MRInstruments - Data sources for genetic instruments to be used in MR

Datasets of eQTLs, GWAS catalogs, etc.

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5.28 score 48 stars 262 scripts

MRMix - Mendelian Randomization Analysis Using Mixture Models (MRMix)

This package gives robust estimation of causal effects by conducting Mendelian randomization analysis using a mixture model approach.

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4.96 score 25 stars 3 dependents 82 scripts

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:10.1080/01621459.1997.10474074>). 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).

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

4.85 score 13 scripts 697 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:10.1080/01621459.1997.10474074>). 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

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

4.85 score 13 scripts 697 downloads

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

4.49 score 2 dependents 46 scripts 1.1k downloads

RMVMR - RMVMR

An R package for performing radial multivariable Mendelian randomization analyses.

Last updated

4.03 score 13 stars 15 scripts

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.

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exercisesknitrrmarkdown

3.65 score 1 stars 1 scripts

quickdag - Make Beautiful Directed Acyclic Graphs

This package allows the user the save directed acyclic graphs (DAGs) generated in DiagrammeR and export them to PDF, PNG, SVG, or PS format. Users may optionally choose to view the DAG without saving or to embed it in a document. In addition, quickdag allows the user easily to reformat DAGs as single-world intervention graph (SWIG) templates.

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causal-inferencediagramsdirected-acyclic-graphsepidemiology

3.18 score 6 stars 7 scripts

tmsens - Sensitivity Analysis Using the Trimmed Means Estimator

Sensitivity analysis using the trimmed means estimator.

Last updated

missing-datasensitivity-analysistrimmed-means

2.70 score 1 stars 6 scripts 305 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).

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2.30 score 2 stars 9 scripts

qbaconfound - Monte Carlo Quantitative Bias Analysis for Unmeasured Confounding

A flexible Monte Carlo quantitative bias analysis (QBA) for unmeasured confounding in observational studies, as described in Hughes et al. The substantive analysis may be a generalised linear model or a Cox proportional hazards model with a binary, continuous, or categorical exposure and measured confounders. The method allows for one or more binary or continuous unmeasured confounders that may be correlated with the measured confounders. Informative priors for a small number of bias parameters encode external information about the unmeasured confounders.

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1.70 score

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

1.70 score 1 scripts