#lqmm used by researchers to model latitudinal gradient of reef-building corals in #science article http://www.sciencemag.org/content/348/6239/1135.short
The new version of lqmm with bug fixes, amendments and new features is now on CRAN. A vignette is also available Geraci M (2014). Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression, Journal of Statistical Software 57(13), 1-29.
An exciting one-day meeting organized by the RSS General Applications Section will take place in London on 29th May 2013. In the morning, the workshop will have a tutorial in quantile regression with hands-on using the R package quantreg. The research session in the afternoon will have three excellent speakers. Full programme and registration details here.
Here is a talk I recently gave at the UCL Department of Statistical Science.
I’ve uploaded lqmm 1.02 into CRAN. A few bugs have been fixed. I made a substantial change to the function boot.lqmm which provides bootstrapped standard errors of the coefficients estimates. The starting values used in each bootstrap sample are by default those from a least squares fit (startQR = FALSE). In previous versions of the package, starting values were taken from the fitted object (startQR = TRUE) to speed up convergence in each bootstrap sample. However, this causes the standard errors to be under-estimated.
The lqmm package in on CRAN! This package provides commands for fitting linear quantile mixed models (LQMMs). LQMMs complement the mixed-effects models for the mean by providing estimation of conditional quantiles as function of fixed and random effects. More details can be found here and here. More posts on lqmm will follow but feel free to drop me a line if you want to know more.