The process of resolving taxon names is necessary when working with biodiversity data. 'APCalign' uses the Australian Plant Census (APC) and the Australian Plant Name Index (APNI) to align and update plant taxon names to current, accepted standards. 'APCalign' also supplies information about the established status of plant taxa across different states/territories.

Analysis of  data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.

The adapted pair correlation function transfers the concept of the
  pair correlation function from point patterns to patterns of objects of
  finite size and irregular shape (e.g. lakes within a country).  The pair
  correlation function describes the spatial distribution of objects, e.g.
  random, aggregated or regularly spaced. This is a reimplementation of the
  method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050>
  using the library 'GEOS'.

It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.

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.