- Recent versions of
`lme4`

(e.g. 1.1-6) give false convergence warnings. There is a summary post on r-sig-mixed-models. If you get warnings about

`max|grad|`

but the model passes this test:

then you are seeing a false-positive warning, and the problem will disappear in future versions (1.1-7 and up).`dd <- fit@optinfo$derivs with(dd,max(abs(solve(Hessian,gradient)))<2e-3)`

- For other warnings (e.g. about the Hessian being singular or having negative eigenvalues), you can try centering and/or scaling continuous predictor variables.
- You can also try (for
`glmer`

fits)`control=glmerControl(optimizer="bobyqa")`

, or use this code to try your problem with a range of optimizers, to see if any of them work better. If your convergence warnings persist, the

`lme4`

maintainers would be happy to hear from you.

- 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:

(These commands install the "master" (development) branch; if you want the release branch from Github add`library("devtools"); install_github("lme4",user="lme4")`

`ref="release"`

to the`install_github()`

call. The`install_github()`

approach requires that you build 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. You may need to specify`build_vignettes=FALSE`

if your system is missing some of the`LaTeX/texi2dvi`

tools.Nearly 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"]]))`

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.- 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 not available for your system, please contact the maintainers).