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Showing content from https://github.com/lme4/lme4 below:

lme4/lme4: Mixed-effects models in R using S4 classes and methods with RcppEigen

• See the NEWS file

• r-sig-mixed-models@r-project.org 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

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.

• 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.

• From CRAN (stable release 1.+)
• 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 r-universe:

install.packages('lme4', repos = c('https://lme4.r-universe.dev', getOption("repos")[["CRAN"]]))

lme4 is developed in a mixture of

• traditional R package building tools, as documented in Writing R Extensions
  • NEWS in inst/NEWS.Rd (not a top-level NEWS.md file)
  • documentation as .Rd files (not roxygen2, although some functions have internal roxygen-style documentation [not used])
  • 'classic' tests in the tests/ directory
  • some Sweave (knitr)/Rnw-format vignette, especially vignettes/lmer.Rnw
• 'tidyverse'-style tools, as documented in R Packages (Wickham and Bryan)
  • testthat tests, in tests/testthat
  • pkgdown web site (via pkgdown.extras, extensions to allow PDF vignettes); trigger manual builds here
• GitHub
  • primary development repository
  • issues
  • testing on GitHub actions (activated by specifying "[run ci]" at the end of a commit message)
  • pull requests are welcome, but please open a discussion as an issue first

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