Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) .

momentuHMM

R package for Maximum likelihood analysis Of animal MovemENT behavior Using multivariate Hidden Markov Models

Get started with the vignette: Guide to using momentuHMM

Installation instructions

CRAN release

The package is available at . To install it:

install.packages("momentuHMM")

Install from Github

To install the latest (stable) version of the package from Github:

library(remotes)
install_github("bmcclintock/momentuHMM")

To install the latest (unstable) version of the package from Github:

library(remotes)
install_github("bmcclintock/momentuHMM@develop")

References

McClintock, B.T., Michelot, T. (2018) momentuHMM: R package for generalized hidden Markov models of animal movement. Methods in Ecology and Evolution, 9(6), 1518-1530.

McClintock, B.T., King R., Thomas L., Matthiopoulos J., McConnell B.J., Morales J.M. (2012) A general discrete-time modeling framework for animal movement using multistate random walks. Ecological Monographs, 82(3), 335-349.

McClintock, B.T. (2017) Incorporating telemetry error into hidden Markov models of animal movement using multiple imputation. Journal of Agricultural, Biological, and Environmental Statistics, 22(3), 249-269.