hdmed

A suite of functions for performing mediation analysis with high-dimensional mediators. In addition to centralizing code from several existing packages for high-dimensional mediation analysis, we provide organized, well-documented functions for a handle of methods which, though programmed their original authors, have not previously been formalized into R packages or been made presentable for public use. The methods we include cover a broad array of approaches and objectives, and are described in detail by both our companion manuscript—"Methods for Mediation Analysis with High-Dimensional DNA Methylation Data: Possible Choices and Comparison"—and the original publications that proposed them. The specific methods offered by our package include the Bayesian sparse linear mixed model (BSLMM) by Song et al. (2019); high-dimensional mediation analysis (HDMA) by Gao et al. (2019); high-dimensional multivariate mediation (HDMM) by Chén et al. (2018); high-dimensional linear mediation analysis (HILMA) by Zhou et al. (2020); high-dimensional mediation analysis (HIMA) by Zhang et al. (2016); latent variable mediation analysis (LVMA) by Derkach et al. (2019); mediation by fixed-effect model (MedFix) by Zhang (2021); pathway LASSO by Zhao & Luo (2022); principal component mediation analysis (PCMA) by Huang & Pan (2016); and sparse principal component mediation analysis (SPCMA) by Zhao et al. (2020). Citations for the corresponding papers can be found in their respective functions.

Last 30 days

This package has been downloaded 497 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

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Last 365 days

This package has been downloaded 6,436 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Sep 11, 2024 with 65 downloads.

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