NLMR is an R package for simulating neutral landscape models (NLM). Designed to be a generic framework like NLMpy, it leverages the ability to simulate the most common NLM that are described in the ecological literature. NLMR builds on the advantages of the raster package and returns all simulation as RasterLayer objects, thus ensuring a direct compatibility to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework.

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R package for some of the less-glamorous tasks involved in landscape analysis. Provides utility functions for some of the less-glamorous tasks involved in landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, ‘landscapetools’ helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks, -bricks and lists of raster.

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landscapemetrics is an R package for calculating landscape metrics for categorical landscape patterns in a tidy workflow. The package can be used as a drop-in replacement for FRAGSTATS (McGarigal et al. 2012), as it offers a reproducible workflow for landscape analysis in a single environment. It also allows for calculations of four theoretical metrics of landscape complexity: an overall spatio-thematic complexity, a thematic complexity, a configurational complexity, and a disambiguator of pattern types having the same overall complexity (Nowosad and Stepinski 2018).

landscapemetrics supports raster spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer as input arguments. Every function can be used in a piped workflow, as it always takes the data as the first argument and returns a tibble.

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The nlrx package provides tools to setup and execute NetLogo simulations from R. NetLogo is a free, open-source and cross-platform modelling environment for simulating natural and social phenomena. NetLogo focusses on implementation of agent-based and spatially explicit simulation models, although system dynamics models are supported as well.

The nlrx package utilizes the commandline functionality of Behavior Space to execute NetLogo simulations directly from R. Instead of defining experiments within NetLogo Behavior Space, experiments are defined in R using the class objects of the nlrx package. These class objects hold all the information that is needed to run these experiments remotely from R, such as path to NetLogo installation folder, path to the model file and the experiment specifications itself. nlrx provides useful helper functions to generate parameter input matrices from parameter range definitions that cover a wide range of parameter exploration approaches. By storing all relevant information on simulation experiments, including the output of the model simulations in one class object, experiments can be easily stored and shared.

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