nlrx

nlrx NetLogo R

https://github.com/ropensci/nlrx

Keywords

agent-based-modeling individual-based-modelling netlogo peer-reviewed r r-package rstats

Keywords from Contributors

air-pollution codemeta json-ld git-lfs data-store noaa-data erddap survey-data defra air-quality

Last synced: 6 months ago · JSON representation

Repository

nlrx NetLogo R

Basic Info

Host: GitHub
Owner: ropensci
License: gpl-3.0
Language: R
Default Branch: master
Homepage: https://docs.ropensci.org/nlrx
Size: 88.5 MB

Statistics

Stars: 79
Watchers: 5
Forks: 13
Open Issues: 18
Releases: 4

Topics

agent-based-modeling individual-based-modelling netlogo peer-reviewed r r-package rstats

Created over 7 years ago · Last pushed over 1 year ago

Metadata Files

Readme Changelog Contributing License Code of conduct Codemeta

README.Rmd

---
output:
  github_document:
    html_preview: false
editor_options: 
  chunk_output_type: console
---



```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# nlrx 




[![R build status](https://github.com/ropensci/nlrx/workflows/R-CMD-check/badge.svg)](https://github.com/ropensci/nlrx/actions)
[![Codecov test coverage](https://codecov.io/gh/ropensci/nlrx/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ropensci/nlrx)
[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html#maturing)
[![CRAN status](https://www.r-pkg.org/badges/version/nlrx)](https://cran.r-project.org/package=nlrx)
[![](http://cranlogs.r-pkg.org/badges/grand-total/nlrx)](https://cran.r-project.org/package=nlrx)
[![ropensci](https://badges.ropensci.org/262_status.svg)](https://github.com/ropensci/software-review/issues/262)
[![DOI:10.1111/2041-210X.13286](https://zenodo.org/badge/DOI/10.1111/2041-210X.13286.svg)](https://doi.org/10.1111/2041-210X.13286)


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 focuses on implementation of agent-based and spatially explicit simulation models, although system dynamics models are supported as well.
NetLogo is developed and maintained at the Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
More details on NetLogo itself are available online: [NetLogo online documentation](https://ccl.northwestern.edu/netlogo/docs/)

NetLogo comes with the built-in experiment tool [Behavior Space](https://ccl.northwestern.edu/netlogo/docs/behaviorspace.html) that allows to setup and execute model simulations with different settings and parameter variations and to collect model output. This experiment tool can be executed via command line in combination with an XML file that contains the experiment specifications, such as runtime, variables, output measurements, stop conditions, and more. One limitation of Behavior Space is, that it only supports full-factorial parameter designs, which may not be appropriate for complex model analyses. Furthermore, Behavior Space experiment specifications are stored within the NetLogo file and are not easily accessible from R. However, in many cases it is useful to store such specifications along with the model output and analyses results in order to enable fully reproducible model analyses.

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.

In summary, the nlrx package uses a similar structure as NetLogos Behavior Space but offers more flexibility and additional tools for running reproducible complex model analyses directly from R.

## Publication
Further information on the package functionality and detailed code examples can be found in our accompanying publication:
Salecker J, Sciaini M, Meyer KM, Wiegand K. The nlrx r package: A next-generation framework for reproducible NetLogo model analyses. Methods Ecol Evol. 2019;2041-210X. [https://doi.org/10.1111/2041-210X.13286](https://doi.org/10.1111/2041-210X.13286).

Get citation information for `nlrx` in R doing `citation(package = 'nlrx')`.

## Prerequirements

### NetLogo
In order to use the nlrx package, [NetLogo](http://netlogoweb.org/) (>=5.3.1) needs to be installed on the system that is used to execute model simulations (local/remote). For remote execution, NetLogo needs to be installed on remote machines as well. The nlrx package provides a utility function (`download_netlogo()`) that can be used to download and unzip (only unix systems) a specified NetLogo version to a local folder. For windows machines, the downloaded file needs to be executed in order to install NetLogo on the local system. If you are running MacOS, please use the Linux tar.gz version of NetLogo (either from the NetLogo Homepage or by using the `download_netlogo()` function). The dmg version from the NetLogo homepage is not compatible with nlrx.

All code snippets on this homepage should be compatible with Netlogo <= 6.2.2. In version 6.3.0, the folder structure of NetLogo was slightly updated, thus the modelpath in the code snippets need to be adjusted accordingly (the `"app/"` folder needs to be removed from the modelpath).

### Java
Because NetLogo is executed in a Java virtual machine, Java needs to be installed on the local/remote system as well. We recommend the [Oracle Java SE Development Kit 8](https://www.oracle.com/java/technologies/javase/javase-jdk8-downloads.html) or the [openjdk](https://github.com/ojdkbuild/ojdkbuild).
While the nlrx package might work without setting the Java system path explicitly, we recommend to make sure that JAVA_HOME points to the correct Java installation of the system.

## Installation

You can install the released version of nlrx from
[CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("nlrx")
```

And the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("ropensci/nlrx")
```

## Get started

General information that is needed to run NetLogo simulations remotely, such as path to the NetLogo installation folder is stored within a `nl` class object.
Nested within this `nl` class are the classes `experiment` and `simdesign`. The `experiment` class stores all experiment specifications. After attaching a valid experiment, a `simdesign` class object can be attached to the `nl` class object, by using one of the simdesign helper functions. These helper functions create different parameter input matrices from the experiment variable definitions that can then be executed by the `run_nl_one()` and `run_nl_all()` functions. The nested design allows to store everything related to the experiment within one R object. Additionally, different simdesign helper functions can be applied to the same `nl` object in order to repeat the same experiment with different parameter exploration methods (simdesigns).

### Step by step application example

The "Wolf Sheep Predation" model from the NetLogo models library is used to present a basic example on how to setup and run NetLogo model simulations from R.

#### Step 1: Create a nl object:

The nl object holds all information on the NetLogo version, a path to the NetLogo directory with the defined version, a path to the model file, and the desired memory for the java virtual machine. Depending on the operation system, paths to NetLogo and the model need to be adjusted.

```{r eval=FALSE}
library(nlrx)
# Windows default NetLogo installation path (adjust to your needs!):
netlogopath <- file.path("C:/Program Files/NetLogo 6.0.3")
modelpath <- file.path(netlogopath, "app/models/Sample Models/Biology/Wolf Sheep Predation.nlogo")
outpath <- file.path("C:/out")
# Unix default NetLogo installation path (adjust to your needs!):
netlogopath <- file.path("/home/NetLogo 6.0.3")
modelpath <- file.path(netlogopath, "app/models/Sample Models/Biology/Wolf Sheep Predation.nlogo")
outpath <- file.path("/home/out")

nl <- nl(nlversion = "6.0.3",
         nlpath = netlogopath,
         modelpath = modelpath,
         jvmmem = 1024)
```

#### Step 2: Attach an experiment

The experiment object is organized in a similar fashion as NetLogo Behavior Space experiments.
It contains all information that is needed to generate a simulation parameter matrix and to execute the NetLogo simulations.
Details on the specific slots of the experiment class can be found in the package documentation (`?experiment`) and the "Advanced configuration" vignette.

```{r eval=FALSE}
nl@experiment <- experiment(expname="wolf-sheep",
                            outpath=outpath,
                            repetition=1,
                            tickmetrics="true",
                            idsetup="setup",
                            idgo="go",
                            runtime=50,
                            evalticks=seq(40,50),
                            metrics=c("count sheep", "count wolves", "count patches with [pcolor = green]"),
                            variables = list('initial-number-sheep' = list(min=50, max=150, qfun="qunif"),
                                             'initial-number-wolves' = list(min=50, max=150, qfun="qunif")),
                            constants = list("model-version" = "\"sheep-wolves-grass\"",
                                             "grass-regrowth-time" = 30,
                                             "sheep-gain-from-food" = 4,
                                             "wolf-gain-from-food" = 20,
                                             "sheep-reproduce" = 4,
                                             "wolf-reproduce" = 5,
                                             "show-energy?" = "false"))
```

#### Step 3: Attach a simulation design

While the experiment defines the variables and specifications of the model, the simulation design creates a parameter input table based on these model specifications and the chosen simulation design method.
nlrx provides a bunch of different simulation designs, such as full-factorial, latin-hypercube, sobol, morris and eFast (see "Simdesign Examples" vignette for more information on simdesigns).
All simdesign helper functions need a properly defined nl object with a valid experiment design. Each simdesign helper also allows to define a number of random seeds that are randomly generated and can be used to execute repeated simulations of the same parameter matrix with different random-seeds (see "Advanced configuration" vignette for more information on random-seed and repetition management).
A simulation design is attached to a nl object by using one of the simdesign helper functions:

```{r eval=FALSE}
nl@simdesign <- simdesign_lhs(nl=nl,
                               samples=100,
                               nseeds=3,
                               precision=3)
```

#### Step 4: Run simulations

All information that is needed to run the simulations is now stored within the nl object.
The `run_nl_one()` function allows to run one specific simulation from the siminput parameter table.
The `run_nl_all()` function runs a loop over all simseeds and rows of the parameter input table siminput.
The loops are constructed in a way that allows easy parallelisation, either locally or on remote HPC machines (see "Advanced configuration" vignette for more information on parallelisation).
Before running your simulations you might want to check your current nl object setup. `eval_variables_constants(nl)` evaluates if the defined variables and constants are correctly defined and are consistent with the attached model. `print(nl)` prints a complete summary of the provided nl object including checkmarks that might help to indicate potential problems.


```{r eval=FALSE}
# Evaluate nl object:
eval_variables_constants(nl)
print(nl)

# Run all simulations (loop over all siminputrows and simseeds)
results <- run_nl_all(nl)
```

#### Step 5: Attach results to nl and run analysis

nlrx provides method specific analysis functions for each simulation design.
Depending on the chosen design, the function reports a tibble with aggregated results or sensitivity indices.
In order to run the analyze_nl function, the simulation output has to be attached to the nl object first.
The simdesign class within the nl object provides a slot for attaching output results (simoutput). An output results tibble can be attached to this slot by using the simdesign setter function `setsim(nl, "simoutput")`.
After attaching the simulation results, these can also be written to the defined outpath of the experiment object.

```{r eval=FALSE}
# Attach results to nl object:
setsim(nl, "simoutput") <- results

# Write output to outpath of experiment within nl
write_simoutput(nl)

# Do further analysis:
analyze_nl(nl)
``` 

## Meta

* Please [report any issues or bugs](https://github.com/ropensci/nlrx/issues/new).
* License: GPL3
* Get citation information for `nlrx` in R doing `citation(package = 'nlrx')`
* We are very open to contributions - if you are interested check [Contributing](https://github.com/ropensci/nlrx/blob/master/CONTRIBUTING.md).
    * Please note that this project is released with a [Contributor Code of Conduct](https://github.com/ropensci/nlrx/blob/master/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.

Owner

Name: rOpenSci
Login: ropensci
Kind: organization
Email: info@ropensci.org
Location: Berkeley, CA

Website: https://ropensci.org/
Twitter: rOpenSci
Repositories: 307
Profile: https://github.com/ropensci

CodeMeta (codemeta.json)

{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "@type": "SoftwareSourceCode",
  "identifier": "nlrx",
  "description": "Setup, run and analyze 'NetLogo' (<https://ccl.northwestern.edu/netlogo/>) model simulations in 'R'. 'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses. The user defines all information that is needed in an intuitive framework, using class objects. Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user. By nesting model calls in future environments, large simulation design with many runs can be executed in parallel. This also enables simulating 'NetLogo' experiments on remote high performance computing machines. In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.",
  "name": "nlrx: Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'",
  "relatedLink": [
    "https://docs.ropensci.org/nlrx/",
    "https://CRAN.R-project.org/package=nlrx"
  ],
  "codeRepository": "https://github.com/ropensci/nlrx/",
  "issueTracker": "https://github.com/ropensci/nlrx/issues/",
  "license": "https://spdx.org/licenses/GPL-3.0",
  "version": "0.4.4",
  "programmingLanguage": {
    "@type": "ComputerLanguage",
    "name": "R",
    "url": "https://r-project.org"
  },
  "runtimePlatform": "R version 4.3.0 (2023-04-21)",
  "provider": {
    "@id": "https://cran.r-project.org",
    "@type": "Organization",
    "name": "Comprehensive R Archive Network (CRAN)",
    "url": "https://cran.r-project.org"
  },
  "author": [
    {
      "@type": "Person",
      "givenName": "Jan",
      "familyName": "Salecker",
      "email": "jan.salecker@posteo.de",
      "@id": "https://orcid.org/0000-0002-9000-4229"
    },
    {
      "@type": "Person",
      "givenName": "Marco",
      "familyName": "Sciaini",
      "email": "sciaini.marco@gmail.com",
      "@id": "https://orcid.org/0000-0002-3042-5435"
    }
  ],
  "maintainer": [
    {
      "@type": "Person",
      "givenName": "Sebastian",
      "familyName": "Hanss",
      "email": "nlrx@mailbox.org"
    }
  ],
  "softwareSuggestions": [
    {
      "@type": "SoftwareApplication",
      "identifier": "testthat",
      "name": "testthat",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=testthat"
    },
    {
      "@type": "SoftwareApplication",
      "identifier": "mockery",
      "name": "mockery",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=mockery"
    },
    {
      "@type": "SoftwareApplication",
      "identifier": "knitr",
      "name": "knitr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=knitr"
    },
    {
      "@type": "SoftwareApplication",
      "identifier": "rmarkdown",
      "name": "rmarkdown",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=rmarkdown"
    },
    {
      "@type": "SoftwareApplication",
      "identifier": "covr",
      "name": "covr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=covr"
    }
  ],
  "softwareRequirements": {
    "1": {
      "@type": "SoftwareApplication",
      "identifier": "XML",
      "name": "XML",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=XML"
    },
    "2": {
      "@type": "SoftwareApplication",
      "identifier": "lhs",
      "name": "lhs",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=lhs"
    },
    "3": {
      "@type": "SoftwareApplication",
      "identifier": "sensitivity",
      "name": "sensitivity",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=sensitivity"
    },
    "4": {
      "@type": "SoftwareApplication",
      "identifier": "dplyr",
      "name": "dplyr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=dplyr"
    },
    "5": {
      "@type": "SoftwareApplication",
      "identifier": "readr",
      "name": "readr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=readr"
    },
    "6": {
      "@type": "SoftwareApplication",
      "identifier": "purrr",
      "name": "purrr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=purrr"
    },
    "7": {
      "@type": "SoftwareApplication",
      "identifier": "furrr",
      "name": "furrr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=furrr"
    },
    "8": {
      "@type": "SoftwareApplication",
      "identifier": "magrittr",
      "name": "magrittr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=magrittr"
    },
    "9": {
      "@type": "SoftwareApplication",
      "identifier": "stats",
      "name": "stats"
    },
    "10": {
      "@type": "SoftwareApplication",
      "identifier": "utils",
      "name": "utils"
    },
    "11": {
      "@type": "SoftwareApplication",
      "identifier": "GenSA",
      "name": "GenSA",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=GenSA"
    },
    "12": {
      "@type": "SoftwareApplication",
      "identifier": "genalg",
      "name": "genalg",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=genalg"
    },
    "13": {
      "@type": "SoftwareApplication",
      "identifier": "raster",
      "name": "raster",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=raster"
    },
    "14": {
      "@type": "SoftwareApplication",
      "identifier": "rstudioapi",
      "name": "rstudioapi",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=rstudioapi"
    },
    "15": {
      "@type": "SoftwareApplication",
      "identifier": "sf",
      "name": "sf",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=sf"
    },
    "16": {
      "@type": "SoftwareApplication",
      "identifier": "tidyr",
      "name": "tidyr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=tidyr"
    },
    "17": {
      "@type": "SoftwareApplication",
      "identifier": "methods",
      "name": "methods"
    },
    "18": {
      "@type": "SoftwareApplication",
      "identifier": "tibble",
      "name": "tibble",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=tibble"
    },
    "19": {
      "@type": "SoftwareApplication",
      "identifier": "crayon",
      "name": "crayon",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=crayon"
    },
    "20": {
      "@type": "SoftwareApplication",
      "identifier": "igraph",
      "name": "igraph",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=igraph"
    },
    "21": {
      "@type": "SoftwareApplication",
      "identifier": "stringr",
      "name": "stringr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=stringr"
    },
    "22": {
      "@type": "SoftwareApplication",
      "identifier": "progressr",
      "name": "progressr",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=progressr"
    },
    "23": {
      "@type": "SoftwareApplication",
      "identifier": "EasyABC",
      "name": "EasyABC",
      "provider": {
        "@id": "https://cran.r-project.org",
        "@type": "Organization",
        "name": "Comprehensive R Archive Network (CRAN)",
        "url": "https://cran.r-project.org"
      },
      "sameAs": "https://CRAN.R-project.org/package=EasyABC"
    },
    "24": {
      "@type": "SoftwareApplication",
      "identifier": "R",
      "name": "R",
      "version": ">= 3.3"
    },
    "SystemRequirements": "\n    NetLogo (>= 5.3.1), Java (>= 11), pandoc, OpenSSL, udunits-2, PROJ.4, GEOS, GDAL, libxml2"
  },
  "fileSize": "1313.759KB",
  "citation": [
    {
      "@type": "ScholarlyArticle",
      "datePublished": "2019",
      "author": [
        {
          "@type": "Person",
          "givenName": "Jan",
          "familyName": "Salecker"
        },
        {
          "@type": "Person",
          "givenName": "Marco",
          "familyName": "Sciaini"
        },
        {
          "@type": "Person",
          "givenName": [
            "Katrin",
            "M."
          ],
          "familyName": "Meyer"
        },
        {
          "@type": "Person",
          "givenName": "Kerstin",
          "familyName": "Wiegand"
        }
      ],
      "name": "The nlrx r package: A next-generation framework for reproducible NetLogo model analyses",
      "identifier": "10.1111/2041-210X.13286",
      "url": "https://doi.org/10.1111/2041-210X.13286",
      "pagination": "2041--210X",
      "@id": "https://doi.org/10.1111/2041-210X.13286",
      "sameAs": "https://doi.org/10.1111/2041-210X.13286",
      "isPartOf": {
        "@type": "PublicationIssue",
        "datePublished": "2019",
        "isPartOf": {
          "@type": [
            "PublicationVolume",
            "Periodical"
          ],
          "volumeNumber": "00",
          "name": "Methods in Ecololgy and Evolution"
        }
      }
    }
  ],
  "releaseNotes": "https://github.com/ropensci/nlrx/blob/master/NEWS.md",
  "readme": "https://github.com/ropensci/nlrx/blob/master/README.md",
  "contIntegration": [
    "https://github.com/ropensci/nlrx/actions",
    "https://app.codecov.io/gh/ropensci/nlrx"
  ],
  "developmentStatus": "https://lifecycle.r-lib.org/articles/stages.html#maturing",
  "review": {
    "@type": "Review",
    "url": "https://github.com/ropensci/software-review/issues/262",
    "provider": "https://ropensci.org"
  },
  "keywords": [
    "peer-reviewed",
    "netlogo",
    "individual-based-modelling",
    "agent-based-modeling",
    "r",
    "r-package",
    "rstats"
  ]
}

GitHub Events

Total

Issues event: 1
Watch event: 2

Last Year

Issues event: 1
Watch event: 2

Committers

Last synced: about 2 years ago

All Time

Total Commits: 525
Total Committers: 11
Avg Commits per committer: 47.727
Development Distribution Score (DDS): 0.339

Past Year

Commits: 29
Committers: 2
Avg Commits per committer: 14.5
Development Distribution Score (DDS): 0.069

Top Committers

Name	Email	Commits
nldoc	j**a@p**e	347
marco	s**o@g**m	132
jsaleck	j**k@g**e	33
Sebastian Hanß	s**n@h**o	4
GitHub Actions	a**s@g**m	3
Jeroen Ooms	j**s@g**m	1
Maëlle Salmon	m**n@y**e	1
Robert Bischoff	b**b@g**m	1
jn	t**i@g**m	1
rOpenSci Bot	m**t@g**m	1
Anna Krystalli	a**i@g**m	1

Committer Domains (Top 20 + Academic)

github.com: 1 hanss.info: 1 gwdg.de: 1 posteo.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 86
Total pull requests: 17
Average time to close issues: 3 months
Average time to close pull requests: 25 days
Total issue authors: 49
Total pull request authors: 7
Average comments per issue: 2.81
Average comments per pull request: 0.29
Merged pull requests: 16
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: 3 days
Issue authors: 1
Pull request authors: 1
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

nldoc (18)
nik1393 (6)
m4x74 (3)
lsaravia (3)
dorazila (2)
z3tt (2)
rfgoldberg (2)
ereineke (2)
dataandcrowd (2)
pndphd (2)
schellheinz (2)
rogerio-bio (2)
bitbacchus (2)
ltdroy (2)
sbacelar (2)

Pull Request Authors

nldoc (10)
maelle (4)
marcosci (2)
katrinleinweber (1)
annakrystalli (1)
bischrob (1)
Nowosad (1)

Top Labels

Issue Labels

bug (8) enhancement (5) question (3) help wanted (3) investigate (1)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- cran 287 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 13
Total maintainers: 1

proxy.golang.org: github.com/ropensci/nlrx

Documentation: https://pkg.go.dev/github.com/ropensci/nlrx#section-documentation
License: gpl-3.0
Latest release: v0.4.4
published almost 3 years ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Stargazers count: 5.6%

Dependent repos count: 5.7%

Average: 5.8%

Forks count: 6.3%

Last synced: 6 months ago

cran.r-project.org: nlrx

Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'

Homepage: https://docs.ropensci.org/nlrx/
Documentation: http://cran.r-project.org/web/packages/nlrx/nlrx.pdf
License: GPL-3
Latest release: 0.4.5
published about 2 years ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 287 Last month

Rankings

Stargazers count: 5.4%

Forks count: 5.6%

Average: 22.9%

Dependent packages count: 29.8%

Dependent repos count: 35.5%

Downloads: 37.9%

Maintainers (1)

nlrx@mailbox.org

Last synced: 6 months ago

nlrx

Science Score: 49.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.Rmd

Owner

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/ropensci/nlrx

Rankings

cran.r-project.org: nlrx

Rankings

Maintainers (1)

Dependencies