Non-Parametric Bayesian Analyses of Animal Movement.
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) 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 model that makes behavioral inference at the observation-level 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. (2021) doi:10.1111/2041-210X.13745.
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:
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:
# install.packages("remotes")
remotes::install_github("joshcullen/bayesmove")
or latest development (unstable) version with:
# 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 if you have not already done so.
For Mac’s, install the Command Line Tools for Xcode by executing
xcode-select --installin 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.