Description
Functional Data Analysis Pipeline, Extracting Functional Traits from Biological Time-Series Data.
Description
Provides a pipeline of tools for analysing circadian time-series data using functional data analysis (FDA). The package supports smoothing of rhythmic time series, functional principle component analysis (FPCA), and extraction of group-level traits from functional representations. Analyses can incorporate multiple curve derivatives and optional temporal segmentation, enabling comparative analysis of circadian dynamics across experimental groups and time windows.
README.md
TimeTraits
TimeTraits provides a set of tools for analysing biological time-series data using functional data analysis (FDA). The package is designed to support end-to-end workflows, from smoothing rhythmic time series to extracting group-level traits from functional principal component analysis (FPCA).
The methods implemented here are particularly suited to circadian and other biological time-series data where interest lies in comparing functional patterns across experimental groups, time windows, or curve derivatives.
Features
- Smoothing of biological time-series data using functional data representations
- Functional principal component analysis (FPCA) of smoothed curves
- Extraction of group-level FPCA-derived traits
- Support for multiple curve derivatives (e.g. 0th, 1st, 2nd)
- Optional temporal segmentation (e.g. pre/post environmental shifts)
- Shape-based outlier detection (where applicable)
Installation
You can install the development version of TimeTraits directly from GitHub:
# install.packages("devtools")
devtools::install_github("scllock/TimeTraits")