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 (in press).
The R package pawacc in now available on CRAN. This package collects functions to read, process and store accelerometer files. Models supported: Actigraph GT1M, GT3X and ActiSleep.
A typical summary statistic for temporal trends is the average percent change (APC). The APC is estimated by using a generalized linear model, usually under the assumption of linearity on the logarithmic scale. A serious limitation of least-squares type estimators is their sensitivity to outliers. We propose a robust and easy-to-compute measure of the temporal trend based on the median of the rates (median percent change – MPC), rather than their mean, under the hypothesis of constant relative change.
An overview on recent advances in transformations toward linearity (Royal Statistical Society 2013 International Conference, Newcastle). Download here.
This paper offers general guidance for conducting quantile regression analysis of complex survey data.
I finally put together a few queries and replies about linear quantile mixed models and the lqmm package