lme4: Mixed-effects models in R.

Recent/release notes

• See the NEWS file

Where to get help

• [email protected] for questions about lme4 usage and more general mixed model questions; please read the info page, and subscribe, before posting ... (note that the mailing list does not support images or large/non-text attachments)
• https://github.com/lme4/lme4/issues for bug, infelicity, and wishlist reporting
• The lme4 tag on StackOverflow for programming-related or the lme4-nlme tag on CrossValidated for statistics-related questions
• maintainer e-mail only for urgent/private communications

Support

If you choose to support lme4 development financially, you can contribute to a fund at McMaster University (home institution of one of the developers) here. The form will say that you are donating to the "Global Coding Fund"; this fund is available for use by the developers, under McMaster's research spending rules. We plan to use the funds, as available, to pay students to do maintenance and development work. There is no way to earmark funds or set up a bounty to direct funding toward particular features, but you can e-mail the maintainers and suggest priorities for your donation.

Features

• Efficient for large data sets, using algorithms from the Eigen linear algebra package via the RcppEigen interface layer.
• Allows arbitrarily many nested and crossed random effects.
• Fits generalized linear mixed models (GLMMs) and nonlinear mixed models (NLMMs) via Laplace approximation or adaptive Gauss-Hermite quadrature; GLMMs allow user-defined families and link functions.
• Incorporates likelihood profiling and parametric bootstrapping.

Installation

On current R (>= 3.0.0)

• From CRAN (stable release 1.0.+)
• Development version from Github:

library("devtools"); install_github("lme4/lme4",dependencies=TRUE)

(This requires devtools >= 1.6.1, and installs the "master" (development) branch.) This approach builds the package from source, i.e. make and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually. Specify build_vignettes=FALSE if you have trouble because your system is missing some of the LaTeX/texi2dvi tools.

• Development binaries from lme4 r-forge repository:

install.packages("lme4",
   repos=c("http://lme4.r-forge.r-project.org/repos",
          getOption("repos")[["CRAN"]]))

(these source and binary versions are updated manually, so may be out of date; if you believe they are, please contact the maintainers).

On old R (pre-3.0.0)

It is possible to install (but not easily to check) lme4 at least as recently as 1.1-7.

• make sure you have exactly these package versions: Rcpp 0.10.5, RcppEigen 3.2.0.2
• for installation, use --no-inst; this is necessary in order to prevent R from getting hung up by the knitr-based vignettes
• running R CMD check is difficult, but possible if you hand-copy the contents of the inst directory into the installed package directory ...

Of lme4.0

• lme4.0 is a maintained version of lme4 back compatible to CRAN versions of lme4 0.99xy, mainly for the purpose of reproducible research and data analysis which was done with 0.99xy versions of lme4.
• there have been some reports of problems with lme4.0 on R version 3.1; if someone has a specific reproducible example they'd like to donate, please contact the maintainers.
• Notably, lme4.0 features getME(<mod>, "..") which is compatible (as much as sensibly possible) with the current lme4's version of getME().
• You can use the convert_old_lme4() function to take a fitted object created with lme4 <1.0 and convert it for use with lme4.0.
• It currently resides on R-forge, and you should be able to install it with

install.packages("lme4.0",
                 repos=c("http://lme4.r-forge.r-project.org/repos",
                         getOption("repos")[["CRAN"]]))

(if the binary versions are out of date or unavailable for your system, please contact the maintainers).