Visualize the Effect of a Continuous Variable on a Time-to-Event Outcome

Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2023) .

Sparse Arrays and Multivariate Polynomials

Sparse arrays interpreted as multivariate polynomials. Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) .

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.

Calculate Slopes of Roads, Rivers and Trajectories

Calculates the slope (longitudinal gradient or steepness) of linear geographic features such as roads (for more details, see Ariza-López et al. (2019) ) and rivers (for more details, see Cohen et al. (2018) ). It can use local Digital Elevation Model (DEM) data or download DEM data via the 'ceramic' package. The package also provides functions to add elevation data to linestrings and visualize elevation profiles.

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 DAG and Associated Node Information

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, and networks-based simulation which can generate even more complex longitudinal and dependent data. For more details, see Robin Denz, Nina Timmesfeld (2025) .

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.

R Object Oriented Programming for Statistical Distribution

Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) . Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) .

Interface Between R and the OpenStreetMap-Based Routing Service OSRM

An interface between R and the 'OSRM' API. 'OSRM' is a routing service based on 'OpenStreetMap' data. See < http://project-osrm.org/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance).