Here you can find the links to the R code that I have written and a brief description of what it does. You may also find related papers (some of them are open access) — when you use the software, please cite them as appropriate. In particular, click here to see how to cite lqmm and any software that explicitly calls lqmm.
lqmm 1.5.3: Fit quantile regression models for independent and hierarchical data
- Geraci M and Bottai M (2007). Quantile regression for longitudinal data using the asymmetric Laplace distribution, Biostatistics 8(1), 140-154.
- Geraci M (2104). Linear quantile mixed models: The lqmm package for Laplace quantile regression, Journal of Statistical Software 57(13), 1-29.
- Geraci M and Bottai M (2014). Linear quantile mixed models, Statistics and Computing 24(3), 461-479.
pawacc 1.2.2: Process, format and store accelerometer data
- Geraci M, Rich C, Sera F, Cortina-Borja M, Grifﬁths LJ, and Dezateux C (2012). Technical report on accelerometry data processing in the Millennium Cohort Study. London, UK: University College London. Available at http://discovery.ucl.ac.uk/1361699.
Qtools 1.2: Collection of utilities for unconditional and conditional quantiles, including functions for quantile transformation models, quantile-based imputation and quantile regression for counts
- Geraci M (2016). Qtools: A collection of models and tools for quantile inference, The R Journal, 8, 117-138.
- Geraci M (2016). Estimation of regression quantiles in complex surveys with data missing at random: An application to birthweight determinants, Statistical Methods in Medical Research, 25(4), 1393-1421.
- Geraci M and Jones MC (2014). Improved transformation-based quantile regression, Canadian Journal of Statistics 43(1), 118-132.
- Dehbi H-M , Cortina-Borja M, and Geraci M (2015). Aranda-Ordaz quantile regression for student performance assessment, Journal of Applied Statistics, doi:10.1080/02664763.2015.1025724.