momentuHMM: R package for generalized hidden Markov models of animal movement. Discrete-time hidden Markov models (HMMs) have become an immensely popular tool for inferring latent animal behaviors from telemetry data. Here we introduce an open-source R package, momentuHMM, that addresses many of the deficiencies in existing HMM software. Features include: 1) data pre-processing and visualization; 2) user-specified probability distributions for an unlimited number of data streams and latent behavior states; 3) biased and correlated random walk movement models, including ”activity centers” associated with attractive or repulsive forces; 4) user-specified design matrices and constraints for covariate modelling of parameters using formulas familiar to most R users; 5) multiple imputation methods that account for measurement error and temporally-irregular or missing data; 6) seamless integration of spatio-temporal covariate raster data; 7) cosinor and spline models for cyclical and other complicated patterns; 8) model checking and selection; and 9) simulation. momentuHMM considerably extends the capabilities of existing HMM software while accounting for common challenges associated with telemetery data. It therefore facilitates more realistic hypothesis-driven animal movement analyses that have hitherto been largely inaccessible to non-statisticians. While motivated by telemetry data, the package can be used for analyzing any type of data that is amenable to HMMs. Practitioners interested in additional features are encouraged to contact the authors

References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. Schafer, Toryn L. J.; Wikle, Christopher K.; VonBank, Jay A.; Ballard, Bart M.; Weegman, Mitch D.: A Bayesian Markov model with Pólya-gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications (2020)
  2. Bulla, Jan (ed.); Langrock, Roland (ed.); Maruotti, Antonello (ed.): Guest editor’s introduction to the special issue on “Hidden Markov models: theory and applications” (2019)
  3. Lawler, Ethan; Whoriskey, Kim; Aeberhard, William H.; Field, Chris; Mills Flemming, Joanna: The conditionally autoregressive hidden Markov model (CarHMM): inferring behavioural states from animal tracking data exhibiting conditional autocorrelation (2019)
  4. Rocio Joo, Matthew E. Boone, Thomas A. Clay, Samantha C. Patrick, Susana Clusella-Trullas, Mathieu Basille: Navigating through the R packages for movement (2019) arXiv
  5. Brett T. McClintock, Theo Michelot: momentuHMM: R package for generalized hidden Markov models of animal movement (2017) arXiv
  6. Hooten, Mevin B. (ed.); King, Ruth (ed.); Langrock, Roland (ed.): Guest editor’s introduction to the special issue on “Animal movement modeling” (2017)
  7. McClintock, Brett T.: Incorporating telemetry error into hidden Markov models of animal movement using multiple imputation (2017)
  8. Scharf, Henry; Hooten, Mevin B.; Johnson, Devin S.: Imputation approaches for animal movement modeling (2017)