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Non-Parametric Bayesian Analyses of Animal Movement

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# bayesmove <img src="man/figures/logo.png" align="right" width=35%  style="padding-left: 10px"/>

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status](https://www.r-pkg.org/badges/version/bayesmove)](https://CRAN.R-project.org/package=bayesmove)
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downloads](https://cranlogs.r-pkg.org/badges/bayesmove)](https://CRAN.R-project.org/package=bayesmove)
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downloads](https://cranlogs.r-pkg.org/badges/grand-total/bayesmove)](https://CRAN.R-project.org/package=bayesmove)

## Introduction

The goal of **bayesmove** is to analyze animal movement using a
non-parametric Bayesian framework, which addresses a number of
limitations of existing segmentation methods and state-space models.
This package currently offers two different model frameworks on which to
make behavioral inference from animal telemetry data: 1) **the
mixed-membership method for movement (M4)** that provides
*segment-level* behavioral state estimation, and 2) **the mixture model
for movement (M3)** that provides *observation-level* behavioral state
estimation.

The M4 model is a two-stage framework that first partitions individual
tracks into segments (via reversible-jump Markov chain Monte Carlo) and
subsequently clusters these segments into latent behavioral states (via
non-parametric Latent Dirichlet Allocation). This framework allows the
analysis of multiple telemetry and biologging data streams, which must
first be discretized into a set of bins before they can be analyzed. The
observation-level M3 model also requires that data streams are first
discretized, but then directly clusters these observations together into
behavioral states within a single step (via a non-parametric mixture
model). While the outcome is similar to that from state-space and hidden
Markov models, this observation-level model does not assume an
underlying Markov property or use a mechanistic process (e.g.,
correlated random walk). Additional details about the M4 method can be
found in Cullen et al. (2022)
[doi:10.1111/2041-210X.13745](https://doi.org/10.1111/2041-210X.13745),
while further details about the M3 method can be found in Valle et
al. (2022) [doi:10.1002/eap.2524](https://doi.org/10.1002/eap.2524).

This package also includes features to check model convergence based on
the log-likelihood for each MCMC iteration. Model output are often
returned in a format that is `tidyverse`-friendly, which allows for easy
visualization using `ggplot2`. Additionally, a Shiny app can be launched
to dynamically and interactively explore animal movement data, including
options to filter and export data from the app.

## Installation

You can install the latest CRAN release with:

``` r
install.packages("bayesmove", dependencies = c("Imports","LinkingTo","Suggests"))
```

which will ensure that all packages needed to run the Shiny app are
installed.

You can install the latest **stable** version of the package from GitHub
with:

``` r
# install.packages("remotes")
remotes::install_github("joshcullen/bayesmove")
```

or latest development (**unstable**) version with:

``` r
# install.packages("remotes")
remotes::install_github("joshcullen/bayesmove@dev")
```

If installing from GitHub, ensure that you have a tool installed for
compiling C++ code:

- For PC’s running Windows, install
  [Rtools](https://cran.r-project.org/bin/windows/Rtools/) if you have
  not already done so.

- For Mac’s, install the [Command Line Tools for
  Xcode](https://developer.apple.com/xcode/resources/) by executing
  `xcode-select --install` in the terminal; or you can download the
  latest version from the URL (free developer registration may be
  required). A full Xcode install uses up a lot of disk space and is not
  required.

## Support

If you are receiving errors from the model output that you believe to be
bugs, please report them as issues in the GitHub repo. Additionally, if
there are any other features you would like added to this package,
please submit them to the issue tracker.

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