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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) segment-level behavioral state estimation and 2) observation-level behavioral state estimation.

The model that makes segment-level inference is a two-stage framework that first partitions individual tracks into segments and subsequently clusters these segments into latent behavioral states. 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. 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).

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.


You can install the latest CRAN release with:


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

# install.packages("remotes")

or latest development (unstable) version with:

# install.packages("remotes")

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


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.