Continuous-time movement modeling
Description: Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data. The package is described in Calabrese & Fleming (2016) and its methods are based on those introduced in Fleming et al (2014-2016) and Péron et al (2016).
Chris H. Fleming and Justin M. Calabrese
Maintainer: Chris H. Fleming <email@example.com>
J. M. Calabrese, C. H. Fleming. (2016). ctmm: an r package for analyzing animal relocation data as a continuous-time stochastic process. Methods in Ecology and Evolution, DOI:10.1111/2041-210X.12559.
C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, and W. F. Fagan. (2014). From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales. The American Naturalist, 183(5), E154-E167.
C. H. Fleming and J. M. Calabrese and T. Mueller and K. A. Olson and P. Leimgruber, and W. F. Fagan (2014). Non-Markovian maximum likelihood estimation of autocorrelated movement processes Methods in Ecology and Evolution, 5(5) 462-472.
C. H. Fleming and Y. Subasi and J. M. Calabrese. (2015). A maximum-entropy description of animal movement. Physical Review E, 91, 032107.
C. H. Fleming and W. F. Fagan and T. Mueller and K. A. Olson and P. Leimgruber, and J. M. Calabrese (2015). Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator. Ecology, 96(5), 1182-1188.
C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, and J. M. Calabrese. (2016). Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data. Ecology, DOI:10.1890/15-1607.
G. Péron, C. H. Fleming, R. C. de Paula, J. M. Calabrese. (2016). Uncovering periodic patterns of space use in animal tracking data with periodograms, including a new algorithm for the Lomb-Scargle periodogram and improved randomization tests. Movement Ecology, 4:19, DOI:10.1186/s40462-016-0084-7.
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