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plmmr

The plmmr (penalized linear mixed models in R) package contains functions that fit penalized linear mixed models to correct for unobserved confounding effects.

Three small datasets ship with plmmr, and tutorials walking through how to analyze these data sets are documented in the plmmr website.

Installation

To install the latest version of the package from GitHub, use this:

devtools::install_github("pbreheny/plmmr")

You can also install plmmr from CRAN:

install.packages('plmmr')

Minimal example

library(plmmr)
X <- rnorm(100*20) |> matrix(100, 20)
y <- rnorm(100)
fit <- plmm(X, y) 
plot(fit)

cvfit <- cv_plmm(X, y)
plot(cvfit)
summary(cvfit)

So how fast is plmmr? And how well does it scale?

These questions are addressed in our manuscript describing plmmr, along with its accompanying GitHub repository. However, using GWAS data from a study with 1,400 samples and 800,000 SNPs, a full plmmr analysis will run in about half an hour using a single core on a laptop.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.