modeval

Modeval is a term used in statistics and data analysis to denote the process of identifying, evaluating, and comparing the modal values, or modes, of a data distribution. While the mode is the most frequently occurring value, modeval focuses on robustness and stability of modes across samples and scales, and on distinguishing multiple modes in multimodal distributions.

Methods employed in modeval include simple frequency counting for discrete data, and density-based approaches for continuous data. Kernel density estimation or kernel smoothing is commonly used to obtain a smooth estimate of the underlying density, after which local maxima are treated as candidate modes. Modeval may also incorporate nonparametric tests to assess multimodality, such as the Silverman test or dip test, and techniques to select bandwidths to balance bias and variance.

To assess reliability, modeval often uses resampling methods, such as bootstrap or subsampling, to produce confidence intervals or stability measures for each mode. Metrics may include modality strength, separation between modes, and mode prominence. In practice, modeval supports reporting the estimated number of modes and their locations, along with uncertainty estimates.

Applications of modeval span exploratory data analysis, clustering, anomaly detection, and signal processing. In clustering, identified modes can serve as initial cluster centers or guidance for density-based clustering algorithms. In quality control, persistent modes may indicate distinct operating regimes; in economics and biology, modes can reflect common states or phenotypes.