biosensors.usc: Distributional Data Analysis Techniques for Biosensor Data

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>.

Version: 1.0
Depends: R (≥ 2.15)
Imports: Rcpp, graphics, stats, methods, utils, energy, fda.usc, parallelDist, osqp, truncnorm
LinkingTo: Rcpp, RcppArmadillo
Suggests: rmarkdown, knitr
Published: 2022-05-05
Author: Juan C. Vidal ORCID iD [aut, cre], Marcos Matabuena ORCID iD [aut], Marta Karas ORCID iD [ctb]
Maintainer: Juan C. Vidal <juan.vidal at>
License: GPL-2
Copyright: see file COPYRIGHTS
NeedsCompilation: yes
Materials: README
CRAN checks: biosensors.usc results


Reference manual: biosensors.usc.pdf
Vignettes: intro_to_package


Package source: biosensors.usc_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): biosensors.usc_1.0.tgz, r-oldrel (arm64): biosensors.usc_1.0.tgz, r-release (x86_64): biosensors.usc_1.0.tgz, r-oldrel (x86_64): biosensors.usc_1.0.tgz


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