Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

- Release 1.1-8 is on CRAN now. There are no major user-visible changes.
- We have fixed some bugs in
`predict`

,`simulate`

, and`refit`

. - Convergence and positive-definite-Hessian warnings are still overly conservative for large (>10^4 rows) data sets, but we are holding off on changing anything until we really understand the problem; see
`help("convergence")`

. - The deviance computation has changed for GLMMs; see "Deviance and log-likelihood of GLMMs" in merMod-class.Rd

- We have fixed some bugs in
- Otherwise, see the NEWS file (or
`news(Version=="1.1.8",package="lme4")`

).

- 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 1.0.+)
- 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.

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(these source and binary versions are updated manually, so may be out of date; if you believe they are, please contact the maintainers).

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 ...

`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).