Software

  1. “gmwm” – R package: provides a computationally efficient implementation of the classical and robust GMWM estimators introduced in Guerrier et al. 2013 (JASA) and Guerrier et al. 2014 (Aust J Stat). Joint work with Balamuta, J., Molinari, R. & Yang, W. More information: https://github.com/SMAC-Group/gmwm.
  2. “panning” – R package: implements the algorithm presented in Guerrier et al. 2016 (Front Genet). Joint work with Balamuta, J., Molinari, R. & Orso, S. More information: https://github.com/SMAC-Group/panning. A more stable version will be available on CRAN early 2017.
  3. “rao” – R package: implements the methods proposed in Jammalamadaka et al. 2016 (submitted manuscript). Joint work with Jammalamadaka, S.R., Balamuta, J., & Yang, W. First stable version will be released on Github late 2016.
  4. “rcopula” – R package: provides a computationally efficient implementation of the indirect inference framework used in Guerrier et al. 2016 (working paper) to estimate copula models in a robust fashion. Joint work with Orso, S. & Balamuta, J. First stable version will be released on Github late 2016.
  5. “imudata” – R package: contains a collection of inertial sensor datasets used in various published papers (e.g. Guerrier et al. 2013 (JASA), Stebler et al. 2014 (IEEE Trans. Aerosp. Electron. Syst.), Stebler et al. 2015 (IEEE Trans. Instrum. Meas.)) to allow to reproducibility of the presented results. Joint work with Balamuta, J. & Molinari, R. More information: https://github.com/SMAC-Group/imudata.
  6. “smacdata” – R package: contains a collection of time series datasets used for teaching purposes. More information: https://github.com/SMAC-Group/smacdata.
  7. “exts” – R package: provides various functions related to time series analysis. This package is essentially used for teaching purposes. Joint work with Balamuta, J. More information: https://github.com/SMAC-Group/exts.