Distributional Data Analysis Techniques for Biosensor Data R package biosensors.usc version 1.0 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. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) <doi:10.1177/0962280221998064>. Software Depends: R (>= 2.15) Imports: Rcpp, graphics, stats, methods, utils, energy, fda.usc, parallelDist, osqp, truncnorm LinkingTo: Rcpp, RcppArmadillo Suggests: rmarkdown, knitr Juan C. Vidal <juan.vidal@usc.es> Comprehensive R Archive Network (CRAN) Juan C. Vidal [aut, cre] (ORCID: <https://orcid.org/0000-0002-8682-6772>), Marcos Matabuena [aut] (ORCID: <https://orcid.org/0000-0003-3841-4447>), Marta Karas [ctb] (ORCID: <https://orcid.org/0000-0001-5889-3970>) GPL-2 2022-05-05 application/tgz https://CRAN.R-project.org/package=biosensors.usc doi:10.32614/CRAN.package.biosensors.usc