Tools to create dynamic, submission-ready manuscripts, which
  conform to American Psychological Association manuscript guidelines. We
  provide R Markdown document formats for manuscripts (PDF and Word) and
  revision letters (PDF). Helper functions facilitate reporting statistical
  analyses or create publication-ready tables and plots.

A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based
	     reports and to prettify the output of various estimated models.

This is a data only package, that provides distances from a paper plane experiment.

A simple interface in the form of R6 classes for executing tasks in
    parallel, tracking their progress, and displaying accurate progress bars.

Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.

Define parameter spaces, constraints and
    dependencies for arbitrary algorithms, to program on such spaces. Also
    includes statistical designs and random samplers. Objects are
    implemented as 'R6' classes.

Creation and selection of PARAllel FACtor Analysis (PARAFAC)
    models of longitudinal microbiome data. You can import your own data with
    our import functions or use one of the example datasets to create your own
    PARAFAC models. Selection of the optimal number of components can be done
    using assessModelQuality() and assessModelStability(). The selected model can
    then be plotted using plotPARAFACmodel(). The Parallel Factor
    Analysis method was originally described by Caroll and Chang (1970)
    <doi:10.1007/BF02310791> and Harshman (1970)
    <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.

A fast parallelized alternative to R's native 'dist' function to
    calculate distance matrices for continuous, binary, and multi-dimensional
    input matrices, which supports a broad variety of 41 predefined distance
    functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
    defined functions written in C++. For ease of use, the 'parDist' function
    extends the signature of the 'dist' function and uses the same parameter
    naming conventions as distance methods of existing R packages. The package
    is mainly implemented in C++ and leverages the 'RcppParallel' package to
    parallelize the distance computations with the help of the 'TinyThread'
    library. Furthermore, the 'Armadillo' linear algebra library is used for
    optimized matrix operations during distance calculations. The curiously
    recurring template pattern (CRTP) technique is applied to avoid virtual
    functions, which improves the Dynamic Time Warping calculations while
    the implementation stays flexible enough to support different DTW step
    patterns and normalization methods.