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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.
Easy Spatial Microsimulation (Raking) in R
Functions for performing spatial microsimulation ('raking') in R.
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.
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.
Linear, Quadratic, and Rational Optimization
Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp'.
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.