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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.14719265 |
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- <p>This archive contains the source code and raw data required for reproducing the figures from the manuscript <strong>"A population Monte Carlo model for underwater acoustic telemetry positioning in reflective environments"</strong>, accepted for publication by the journal <em>Methods in Ecology and Evolution</em> on <strong>2025-01-20</strong>.</p> <p>All analysis scripts are located in the root directory, and are prefixed by <code>script_</code>. Here, scripts are required to be run in the order indicated by the numbers in their file names.</p> <p>There are two analysis here, the real case study,<code>_casestudy_</code>, and the simulated analysis, <code>_simstudy_</code>. The results for each analysis are returned as <code>png</code> files in the directory <code>./figures</code>. Each analysis script will return a serialized R data object, <code>rds</code>, which will in turn be used by the next numbered script in that analysis.</p> <p>A <code>Makefile</code> and <code>Rproj</code> file are included. On UNIX systems, one can simply open the R project and execute the Makefile in R studio by pressing <code>Build All</code> under the build panel. Alternatively, the makefile can be called form the terminal with <code>$ make all</code>. If the makefile doesn't work on your system, you can simply make a directory named <code>./figures</code> to hold the results and then run each script in order for a given analysis.</p> <p>The following R packages are required for this analysis. The versions numbers used in the manuscript are given.</p> <ul> <li> <p>sf: 1.0-16</p> </li> <li> <p>chi: 0.1</p> </li> <li> <p>data.table: 1.16.4</p> </li> <li> <p>yaps: 1.2.5</p> </li> <li> <p>terra: 1.7-71</p> </li> <li> <p>grDevices: 4.4.0</p> </li> <li> <p>mvtnorm: 1.2-4</p> </li> </ul> <p>Note that YAPS was archived from the CRAN at the time of writing.</p> <p>The directory <code>./code</code> holds functions that may be of interest to others.</p> <ul> <li> <p><code>ctcrw_main.R</code> holds a custom implementation of a continuous-time correlated random walk model. Here, measurement error covariance matrices can be passed directly to the Kalman filter, unlike the implementation provided in the <code>crawl</code> package.</p> </li> <li> <p><code>gnorm.R</code> holds the PDF and CDF functions for the generalized normal distribution.</p> </li> <li> <p><code>TDOA.R</code> holds functions for time-difference-of-arrival positioning, along with a closed-form solutions for the Jacobian of the TDOA function used in the analysis.</p> </li> <li> <p><code>pmc_main.R</code> & <code>pmc_internal.R</code> hold the functions for population Monte Carlo positioning.</p> </li> </ul> <p>Examples of how to use these functions are given in the supplementary material document, <code>Supplementary_material.rmd</code>.</p>