Package: moveHMM 1.9

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, etc. <doi:10.1111/2041-210X.12578>.

Authors:Theo Michelot, Roland Langrock, Toby Patterson, Brett McClintock, Eric Rexstad

moveHMM_1.9.tar.gz
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moveHMM_1.9.tgz(r-4.4-x86_64)moveHMM_1.9.tgz(r-4.4-arm64)moveHMM_1.9.tgz(r-4.3-x86_64)moveHMM_1.9.tgz(r-4.3-arm64)
moveHMM_1.9.tar.gz(r-4.5-noble)moveHMM_1.9.tar.gz(r-4.4-noble)
moveHMM_1.9.tgz(r-4.4-emscripten)moveHMM_1.9.tgz(r-4.3-emscripten)
moveHMM.pdf |moveHMM.html✨
moveHMM/json (API)

# Install 'moveHMM' in R:

install.packages('moveHMM', repos = c('https://theomichelot.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/theomichelot/movehmm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

elk_data - Elk data set from Morales et al.
example - Example dataset
haggis_data - Wild haggis data set from Michelot et al.

On CRAN:

8.61 score 38 stars 107 scripts 699 downloads 22 mentions 17 exports 56 dependencies

Last updated 8 months agofrom:223478e7ca. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 11 2024
R-4.5-win-x86_64	NOTE	Nov 11 2024
R-4.5-linux-x86_64	NOTE	Nov 11 2024
R-4.4-win-x86_64	NOTE	Nov 11 2024
R-4.4-mac-x86_64	NOTE	Nov 11 2024
R-4.4-mac-aarch64	NOTE	Nov 11 2024
R-4.3-win-x86_64	NOTE	Nov 11 2024
R-4.3-mac-x86_64	NOTE	Nov 11 2024
R-4.3-mac-aarch64	NOTE	Nov 11 2024

Exports:CI fitHMM getPlotData nLogLike plotPR plotSat plotStates plotStationary predictStationary predictTPM prepData pseudoRes simData splitAtGaps stateProbs stationary viterbi

Dependencies:askpass bitops boot CircStats cli colorspace cpp11 curl digest dplyr fansi farver generics geosphere ggmap ggplot2 glue gtable httr isoband jpeg jsonlite labeling lattice lifecycle magrittr MASS Matrix mgcv mime munsell nlme numDeriv openssl pillar pkgconfig plyr png purrr R6 RColorBrewer Rcpp RcppArmadillo rlang scales sp stringi stringr sys tibble tidyr tidyselect utf8 vctrs viridisLite withr

Choosing starting values in moveHMM

Rendered frommoveHMM-starting-values.Rnwusingknitr::knitron Nov 11 2024.

Last update: 2023-05-06
Started: 2019-05-18

Custom plots with moveHMM

Rendered frommoveHMM-custom-plots.Rnwusingknitr::knitron Nov 11 2024.

Last update: 2019-05-18
Started: 2019-05-02

Guide to using moveHMM

Rendered frommoveHMM-guide.Rnwusingknitr::knitron Nov 11 2024.

Last update: 2022-05-06
Started: 2015-11-09

moveHMM workflow: wild haggis analysis

Théo Michelot

Rendered frommoveHMM-example.Rmdusingknitr::rmarkdownon Nov 11 2024.

Last update: 2022-05-13
Started: 2022-05-10

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
AIC	AIC.moveHMM
Confidence intervals for angle parameters	angleCI
Confidence intervals	CI
Exponential density function	dexp_rcpp
Gamma density function	dgamma_rcpp
Log-normal density function	dlnorm_rcpp
Von Mises density function	dvm_rcpp
Weibull density function	dweibull_rcpp
Wrapped Cauchy density function	dwrpcauchy_rcpp
Elk data set from Morales et al. (2004, Ecology)	elk_data
Example dataset	example
Example data simulation	exGen
Fit an HMM to the data	fitHMM
Discrete colour palette for states	getPalette
Data to produce plots of fitted model	getPlotData
Wild haggis data set from Michelot et al. (2016, Methods Eco Evol)	haggis_data
Is moveData	is.moveData
Is moveHMM	is.moveHMM
Forward log-probabilities	logAlpha
Backward log-probabilities	logBeta
Constructor of 'moveData' objects	moveData
Constructor of 'moveHMM' objects	moveHMM
Scaling function: natural to working parameters.	n2w
Negative log-likelihood function	nLogLike
Negative log-likelihood	nLogLike_rcpp
Parameters definition	parDef
Plot 'moveData'	plot.moveData
Plot 'moveHMM'	plot.moveHMM
Plot pseudo-residuals	plotPR
Plot observations on satellite image	plotSat
Plot states	plotStates
Plot stationary state probabilities	plotStationary
Predict stationary state probabilities	predictStationary
Predict transition probabilities for new covariate values	predictTPM
Preprocessing of the tracking data	prepData
Print 'moveHMM'	print.moveHMM
Pseudo-residuals	pseudoRes
Simulation tool	simData
Split track at gaps	splitAtGaps
State probabilities	stateProbs
Stationary state probabilities	stationary
Summary 'moveData'	summary.moveData
Transition probability matrix	trMatrix_rcpp
Turning angle	turnAngle
Viterbi algorithm	viterbi
Scaling function: working to natural parameters	w2n