[code]Conclusion
Fitting mixed effect models and exploring group level variation is very easy within the R language and ecosystem. In future tutorials we will explore comparing across models, doing inference with mixed-effect models, and creating graphical representations of mixed effect models to understand their effects.

Appendix
print(sessionInfo(), locale = FALSE)
## R version 3.0.1 (2013-05-16)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
##
## other attached packages:
## [1] plyr_1.8        arm_1.6-10      MASS_7.3-29     lme4_1.0-5     
## [5] Matrix_1.1-0    lattice_0.20-24 knitr_1.5      
##
## loaded via a namespace (and not attached):
##  [1] abind_1.4-0    coda_0.16-1    evaluate_0.5.1 formatR_0.10  
##  [5] grid_3.0.1     minqa_1.2.1    nlme_3.1-113   splines_3.0.1
##  [9] stringr_0.6.2  tools_3.0.1
[1] Examples include Gelman and Hill, Gelman et al. 2013, etc.

thank you