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 
[aut, cre],
Marcos Matabuena
[aut],
Marta Karas [ctb] |
| Maintainer: |
Juan C. Vidal <juan.vidal at usc.es> |
| License: |
GPL-2 |
| Copyright: |
see file COPYRIGHTS |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
biosensors.usc results |
Documentation:
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