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Weierstrass and Jacobi Elliptic Functions
A suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions.
The Symmetric Group: Permutations of a Finite Set
Manipulates invertible functions from a finite set to
itself. Can transform from word form to cycle form and
back. To cite the package in publications please use
Hankin (2020) "Introducing the permutations R package",
SoftwareX, volume 11
Alluvial Diagrams
Creating alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data.
Easy Spatial Microsimulation (Raking) in R
Functions for performing spatial microsimulation ('raking') in R.
Simulate Data from a (Time-Dependent) Causal DAG
Simulate complex data from a given directed acyclic graph and information about each individual node.
Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to
one of many implemented regressions, such as logistic regression, linear
regression, poisson regression or any other function. Also includes a comprehensive framework for discrete-time
simulation, discrete-event simulation, and networks-based simulation which can generate even more complex longitudinal and dependent data.
For more details, see Robin Denz, Nina Timmesfeld (2026)
Meta-Package for Thematic Mapping with 'tmap'
Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem.
Sparse Arrays and Multivariate Polynomials
Sparse arrays interpreted as multivariate polynomials.
Uses 'disordR' discipline (Hankin, 2022,
Very Large Numbers in R
Very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. Functionality for complex numbers is also provided. The package includes a vignette that gives a step-by-step introduction to using S4 methods.
Optimal Exact Tests for Multiple Binary Endpoints
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints. The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures.
Optimum Contribution Selection and Population Genetics
A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009)