Provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters."

Simplifies output suppression logic in R packages, as it's common
    to develop some form of it in R. 'quietR' intends to simplify that problem 
    and allow a set of simple toggle functions to be used to suppress console 
    output.

Functions to convert text data for labelling into format appropriate for importing into Qualtrics. Supports multiple language, including right-to-left scripts as well as different response types. Outputs an Advance Format .txt file that read into Qualtrics.

Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial. More information in Dusseldorp, E., Doove, L., & Van Mechelen, I. (2016) <doi:10.3758/s13428-015-0594-z>.

Functions to compute Euclidean minimum spanning trees using single-,
    sesqui-, and dual-tree Boruvka algorithms.  Thanks to K-d trees, they are
    fast in spaces of low intrinsic dimensionality.  Mutual reachability
    distances (used in the definition of the 'HDBSCAN*' algorithm)
    are also supported.  The package also features relatively fast fallback
    minimum spanning tree and nearest-neighbours algorithms for spaces of
    higher dimensionality.  The 'Python' version of 'quitefastmst' is available
    via 'PyPI'.

To construct a model in 2-D space from 2-D nonlinear
    dimension reduction data and then lift it to the high-dimensional
    space. Additionally, provides tools to visualise the model overlay the
    data in 2-D and high-dimensional space. Furthermore, provides
    summaries and diagnostics to evaluate the nonlinear dimension
    reduction layout.