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.

Exploratory Data Analysis for the 'spatstat' Family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

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) .

Variable Selection Using Random Forests

Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) < https://journal.r-project.org/articles/RJ-2015-018/>.

The Hyperdirichlet Distribution, Mark 2

A suite of routines for the hyperdirichlet distribution and reified Bradley-Terry; supersedes the 'hyperdirichlet' package; uses 'disordR' discipline . To cite in publications please use Hankin 2017 , and for Generalized Plackett-Luce likelihoods use Hankin 2024 .

Fast Symbolic Multivariate Polynomials

Fast manipulation of symbolic multivariate polynomials using the 'Map' class of the Standard Template Library. The package uses print and coercion methods from the 'mpoly' package but offers speed improvements. It is comparable in speed to the 'spray' package for sparse arrays, but retains the symbolic benefits of 'mpoly'. To cite the package in publications, use Hankin 2022 . Uses 'disordR' discipline.

Extension to 'tmap' for Creating Network Visualizations

Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions.

Interface Between 'GRASS' Geographical Information System and 'R'

An interface between the 'GRASS' geographical information system ('GIS') and 'R', based on starting 'R' from within the 'GRASS' 'GIS' environment, or running a free-standing 'R' session in a temporary 'GRASS' location; the package provides facilities for using all 'GRASS' commands from the 'R' command line. The original interface package for 'GRASS 5' (2000-2010) is described in Bivand (2000) and Bivand (2001) < https://www.r-project.org/conferences/DSC-2001/Proceedings/Bivand.pdf>. This was succeeded by 'spgrass6' for 'GRASS 6' (2006-2016) and 'rgrass7' for 'GRASS 7' (2015-present). The 'rgrass' package modernizes the interface for 'GRASS 8' while still permitting the use of 'GRASS 7'.

Cluster Origin-Destination Flow Data

Provides functionality for clustering origin-destination (OD) pairs, representing desire lines (or flows). This includes creating distance matrices between OD pairs and passing distance matrices to a clustering algorithm. See the academic paper Tao and Thill (2016) for more details on spatial clustering of flows. See the paper on delineating demand-responsive operating areas by Mahfouz et al. (2025) for an example of how this package can be used to cluster flows for applied transportation research.

Simultaneous Inference for Multiple Linear Contrasts in GEE Models

Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.