https://github.com/g-rppl/movetrack

Estimate flight tracks from radio-telemetry data using a Hidden Markov Model.
https://github.com/g-rppl/movetrack

hmm motus movement-ecology movement-modeling r random-walk stan telemetry

Last synced: about 1 month ago
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Estimate flight tracks from radio-telemetry data using a Hidden Markov Model.

• Host: GitHub
• URL: https://github.com/g-rppl/movetrack
• Owner: g-rppl
• License: mit
• Created: 2023-11-29T10:42:21.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-13T08:00:39.000Z (7 months ago)
• Last Synced: 2024-05-13T08:25:02.360Z (7 months ago)
• Topics: hmm, motus, movement-ecology, movement-modeling, r, random-walk, stan, telemetry
• Language: R
• Homepage: https://g-rppl.github.io/movetrack
• Size: 6.88 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# movetrack

[![R-CMD-check](https://github.com/g-rppl/movetrack/workflows/R-CMD-check/badge.svg)](https://github.com/g-rppl/movetrack/actions)

[![codecov](https://codecov.io/gh/g-rppl/movetrack/branch/main/graph/badge.svg)](https://app.codecov.io/gh/g-rppl/movetrack)

[![Universe](https://g-rppl.r-universe.dev/badges/movetrack)](https://g-rppl.r-universe.dev/movetrack)

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/g-rppl/movetrack/blob/main/LICENSE)

`movetrack` is an `R` package that provides simple functionality to estimate individual flight tracks from radio-telemetry data such as [Motus](https://motus.org/) using random walk models written in [Stan](https://mc-stan.org/).

## Installation

You can install `movetrack` from the R Universe with

```r

install.packages("movetrack", repos = c("https://g-rppl.r-universe.dev", getOption("repos")))

```

To instead install the latest development version of the package from GitHub use

```r

devtools::install_github("g-rppl/movetrack@dev")

```

During the initial installation, make sure that the C++ toolchain required for `CmdStan` is set up properly. You can find more information [here](https://mc-stan.org/cmdstanr/articles/cmdstanr.html).

```r

library(cmdstanr)

check_cmdstan_toolchain(fix = TRUE)

```

If not, go to  and follow the instructions for your platform. Once your toolchain is configured correctly `CmdStan` can be installed:

```r

install_cmdstan(cores = 2)

```

## Details

This package provides two main functions: `locate()` and `track()`. The first function calculates location estimates based on antenna bearing and signal strength. The second function estimates individual flight paths based on the estimated locations using a Hidden Markov Model written in [Stan](https://mc-stan.org/).

## Getting started

    

You can find a quickstart example in the vignette [movetrack_example](https://g-rppl.github.io/movetrack/articles/movetrack_example.html).

## References

Auger‐Méthé, M., Newman, K., Cole, D., Empacher, F., Gryba, R., King, A. A., ... & Thomas, L. (2021). A guide to state–space modeling of ecological time series. *Ecological Monographs*, 91(4), e01470. doi:[10.1002/ecm.1470](https://doi.org/10.1002/ecm.1470)

Baldwin, J. W., Leap, K., Finn, J. T., & Smetzer, J. R. (2018). Bayesian state-space models reveal unobserved off-shore nocturnal migration from Motus data. *Ecological Modelling*, 386, 38-46. doi:[10.1016/j.ecolmodel.2018.08.006](https://doi.org/10.1016/j.ecolmodel.2018.08.006)

Jonsen, I. D., Flemming, J. M., & Myers, R. A. (2005). Robust state–space modeling of animal movement data. *Ecology*, 86(11), 2874-2880. doi:[10.1890/04-1852](https://doi.org/10.1890/04-1852)