Package: mvfmr 0.1.0

Nicole Fontana

mvfmr: Functional Multivariable Mendelian Randomization

Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.

Authors:Nicole Fontana [aut, cre], Francesca Ieva [aut, ths], Piercesare Secchi [aut, ths]

mvfmr_0.1.0.tar.gz
mvfmr_0.1.0.tar.gz(r-4.7-any)mvfmr_0.1.0.tar.gz(r-4.6-any)
mvfmr_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mvfmr/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 228 downloads 11 exports 76 dependencies

Last updated from:3be5577de6. Checks:4 OK. Indexed: yes.

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linux-devel-x86_64OK179
source / vignettesOK389
linux-release-x86_64OK170
wasm-releaseOK137

Exports:cf_logitfmvmr_separate_twosamplefmvmr_twosamplegetX_multi_exposuregetX_multi_exposure_mediationgetY_multi_exposuregmm_lm_onesamplegmm_twosample_simpleISmvfmrmvfmr_separate

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayondata.tabledigestdoParallelevaluatefarverfastmapfdapacefontawesomeforeachforeignFormulafsggplot2glmnetgluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemimennetnumDerivpkgconfigpracmaprettyunitspROCprogressR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrpartrstudioapiS7sassscalesshapestringistringrsurvivaltinytexvctrsviridisLitewithrxfunyaml

Introduction to Multivariable Functional Mendelian Randomization
Overview | Key Features | When to Use MV-FMR | Installation | Example: Joint Estimation of Two Exposures | Step 1: Simulate Data | Step 2: Generate Outcome | Step 3: Functional Principal Component Analysis | Step 4: Joint Multivariable Estimation | Step 5: Visualize Time-Varying Effects | Step 6: Extract Coefficients | Step 7: Performance Metrics | Comparison: Joint vs. Separate Estimation | Performance Comparison | Instrument Strength Diagnostics | Binary Outcomes | Next Steps | Learn More | Citation | Session Info
Rendered frommultivariable-fmr.Rmdusingknitr::rmarkdown

Last update: 2026-02-09
Started: 2026-02-09

Univariable Functional Mendelian Randomization
Overview | When to Use U-FMR | Installation | Example: Single Exposure Analysis | Step 1: Simulate Data | Step 2: Generate Outcome | Step 3: FPCA for Single Exposure | Step 4: Univariable Estimation | Step 5: Visualize Time-Varying Effect | Step 6: Extract Results | Step 7: Performance Metrics | Binary Outcomes | Advanced Topics | Available Effect Models | Bootstrap Inference | Two-Sample Design | Next Steps | Learn More | Citation | Session Info
Rendered fromunivariable-fmr.Rmdusingknitr::rmarkdown

Last update: 2026-02-09
Started: 2026-02-09