R package moveHMM: Animal Movement Modelling using Hidden Markov Models. Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process...

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. 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)
  2. 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)
  3. Potts, Jonathan R.; Lewis, Mark A.: Spatial memory and taxis-driven pattern formation in model ecosystems (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. Johannes Signer, John Fieberg, Tal Avgar: Animal Movement Tools (amt): R-Package for Managing Tracking Data and Conducting Habitat Selection Analyses (2018) arXiv
  6. Brett T. McClintock, Theo Michelot: momentuHMM: R package for generalized hidden Markov models of animal movement (2017) arXiv
  7. Hooten, Mevin B. (ed.); King, Ruth (ed.); Langrock, Roland (ed.): Guest editor’s introduction to the special issue on “Animal movement modeling” (2017)
  8. McClintock, Brett T.: Incorporating telemetry error into hidden Markov models of animal movement using multiple imputation (2017)
  9. Parton, A.; Blackwell, P. G.: Bayesian inference for multistate `step and turn’ animal movement in continuous time (2017)
  10. Patterson, Toby A.; Parton, Alison; Langrock, Roland; Blackwell, Paul G.; Thomas, Len; King, Ruth: Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges (2017)
  11. Pohle, Jennifer; Langrock, Roland; van Beest, Floris M.; Schmidt, Niels Martin: Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement (2017)
  12. Zucchini, Walter; MacDonald, Iain L.; Langrock, Roland: Hidden Markov models for time series. An introduction using R (2016)