beeRapp

beeRapp performs automated high-throughput exploratory analysis of multivariate behavioral data for animal behavioral studies.

Key Features:

• R Shiny implementation: Implemented as an R Shiny application for computational analysis of behavioral datasets.
• Automated analysis techniques: Supports clustering, dimensionality reduction (e.g., principal component analysis), and inferential statistics.
• Visualization capabilities: Produces large numbers of output plots including boxplots, heatmaps, correlation matrices, and pairwise comparisons.
• High-throughput processing: Automates analysis workflows to handle large volumes of multivariate behavioral data.
• Standardization and reproducibility: Provides standardized analysis and visualization workflows to support reproducible results.

Scientific Applications:

• Animal behavioral studies: Exploratory and inferential analysis of high-dimensional, multivariate behavioral datasets to identify patterns, relationships, and correlations.

Methodology:

Computational methods explicitly include clustering, dimensionality reduction (principal component analysis), inferential statistics, and generation of boxplots, heatmaps, correlation matrices, and pairwise comparisons; implemented as an R Shiny application.