Implements Affinity Propagation clustering introduced by Frey and
	Dueck (2007) <DOI:10.1126/science.1136800>. The algorithms are largely
        analogous to the 'Matlab' code published by Frey and Dueck.
        The package further provides leveraged affinity propagation and an
        algorithm for exemplar-based agglomerative clustering that can also be
        used to join clusters obtained from affinity propagation. Various
        plotting functions are available for analyzing clustering results.

In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101.

Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments.
    The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis.
    Sophisticated functions are available both for descriptive and regression model-based analyses.
    For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization
    techniques building on the concept of Lexis diagrams.
    Model-based analyses build on the separation of the temporal dimensions based on generalized additive models,
    where a tensor product interaction surface (usually between age and period) is utilized
    to represent the third dimension (usually cohort) on its diagonal.
    Such tensor product surfaces can also be estimated while accounting for
    further covariates in the regression model.
    See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.