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NetLogoR  

Build and Run Spatially Explicit Agent-Based Models
View on CRAN: Click here


Download and install NetLogoR package within the R console
Install from CRAN:
install.packages("NetLogoR")

Install from Github:
library("remotes")
install_github("cran/NetLogoR")

Install by package version:
library("remotes")
install_version("NetLogoR", "1.0.5")



Attach the package and use:
library("NetLogoR")
Maintained by
Eliot J B McIntire
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-02-05
Latest Update: 2022-11-09
Description:
Build and run spatially explicit agent-based models using only the R platform. 'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky (1999) ) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants ) and Wolf-Sheep-Predation (), and a third, Butterfly, from Railsback and Grimm (2012) , all written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide () and a dictionary of 'NetLogo' primitives () equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (). The suggested package 'fastshp' can be installed with 'install.packages("fastshp", repos = (""), type = "source")'.
How to cite:
Eliot J B McIntire (2018). NetLogoR: Build and Run Spatially Explicit Agent-Based Models. R package version 1.0.5, https://cran.r-project.org/web/packages/NetLogoR. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.3.1 (2018-02-05 11:03), 0.3.2 (2018-02-12 16:40), 0.3.3 (2018-06-07 20:42), 0.3.4 (2018-10-07 20:00), 0.3.5 (2019-01-25 18:20), 0.3.6 (2019-11-27 13:00), 0.3.7 (2020-03-02 11:00), 0.3.8 (2021-07-10 12:40), 0.3.9 (2021-10-19 19:00), 0.3.10 (2022-02-18 09:20), 0.3.11 (2022-08-17 09:30)
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