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.

An implementation of the additive polynomial (AP) design matrix. It
    constructs and appends an AP design matrix to a data frame for use with
    longitudinal data subject to seasonality.

Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.