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biosensors.usc  

Distributional Data Analysis Techniques for Biosensor Data
View on CRAN: Click here


Download and install biosensors.usc package within the R console
Install from CRAN:
install.packages("biosensors.usc")

Install from Github:
library("remotes")
install_github("cran/biosensors.usc")

Install by package version:
library("remotes")
install_version("biosensors.usc", "1.0")



Attach the package and use:
library("biosensors.usc")
Maintained by
Juan C. Vidal
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-05
Latest Update: 2022-05-05
Description:
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) .
How to cite:
Juan C. Vidal (2022). biosensors.usc: Distributional Data Analysis Techniques for Biosensor Data. R package version 1.0, https://cran.r-project.org/web/packages/biosensors.usc
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