fabian-s/biosensors.usc

biosensors.usc

Biosensor.usc aims to provide a unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI.

Please use reference [1] to cite this package.

Bibliography:

1. Matabuena, M., Petersen, A., Vidal, J. C., & Gude, F. (2021). Glucodensities: A new representation of glucose profiles using distributional data analysis. Statistical methods in medical research, 0962280221998064.

2. Matabuena, M., & Petersen, A. (2021). Distributional data analysis with accelerometer data in a NHANES database with nonparametric survey regression models. arXiv preprint arXiv:2104.01165.