Package: moveHMM 1.12

Theo Michelot

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 [aut, cre], Roland Langrock [aut, ctb], Toby Patterson [aut, ctb], Brett McClintock [ctb], Eric Rexstad [ctb]

moveHMM_1.12.tar.gz
moveHMM_1.12.zip(r-4.7)moveHMM_1.12.zip(r-4.6)moveHMM_1.12.zip(r-4.5)
moveHMM_1.12.tgz(r-4.6-x86_64)moveHMM_1.12.tgz(r-4.6-arm64)moveHMM_1.12.tgz(r-4.5-x86_64)moveHMM_1.12.tgz(r-4.5-arm64)
moveHMM_1.12.tar.gz(r-4.7-arm64)moveHMM_1.12.tar.gz(r-4.7-x86_64)moveHMM_1.12.tar.gz(r-4.6-arm64)moveHMM_1.12.tar.gz(r-4.6-x86_64)
moveHMM_1.12.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
moveHMM/json (API)

# Install 'moveHMM' in R:
install.packages('moveHMM', repos = c('https://benjaminhlina.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:

On CRAN:

Conda:

animal-movementhidden-markov-modelhmmmovement-ecologyopenblascpp

9.61 score 44 stars 153 scripts 723 downloads 22 mentions 17 exports 50 dependencies

Last updated from:442d833de3. Checks:13 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK186
linux-devel-x86_64OK181
source / vignettesOK272
linux-release-arm64OK168
linux-release-x86_64OK189
macos-release-arm64OK124
macos-release-x86_64OK349
macos-oldrel-arm64OK108
macos-oldrel-x86_64OK271
windows-develOK177
windows-releaseOK161
windows-oldrelOK245
wasm-releaseOK126

Exports:CIfitHMMgetPlotDatanLogLikeplotPRplotSatplotStatesplotStationarypredictStationarypredictTPMprepDatapseudoRessimDatasplitAtGapsstateProbsstationaryviterbi

Dependencies:askpassbitopsbootclicpp11curldigestdplyrfarvergenericsgeosphereggmapggplot2gluegtablehttrisobandjpegjsonlitelabelinglatticelifecyclemagrittrMASSmimenumDerivopensslpillarpkgconfigplyrpngpurrrR6RColorBrewerRcppRcppArmadillorlangS7scalesspstringistringrsystibbletidyrtidyselectutf8vctrsviridisLitewithr

Custom plots with moveHMM
Rendered frommoveHMM-custom-plots.Rnwusingknitr::knitr

Last update: 2025-06-18
Started: 2019-05-02

Choosing starting values in moveHMM
Rendered frommoveHMM-starting-values.Rnwusingknitr::knitr

Last update: 2025-06-17
Started: 2019-05-18

Guide to using moveHMM
Rendered frommoveHMM-guide.Rnwusingknitr::knitr

Last update: 2025-06-17
Started: 2015-11-09

moveHMM workflow: wild haggis analysis
Data preparation | Model fitting | Model visualisation | State process inference | Covariates | Model checking | Custom plots | Other features | predict functions | knownStates | fit = FALSE | References
Rendered frommoveHMM-example.Rmdusingknitr::rmarkdown

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

Readme and manuals

Help Manual

Help pageTopics
AICAIC.moveHMM
Confidence intervals for angle parametersangleCI
Confidence intervalsCI
Exponential density functiondexp_rcpp
Gamma density functiondgamma_rcpp
Log-normal density functiondlnorm_rcpp
Density function of von Mises distributiondvm
Von Mises density functiondvm_rcpp
Weibull density functiondweibull_rcpp
Density function of wrapped Cauchy distributiondwrpcauchy
Wrapped Cauchy density functiondwrpcauchy_rcpp
Elk data set from Morales et al. (2004, Ecology)elk_data
Example datasetexample
Example data simulationexGen
Fit an HMM to the datafitHMM
Discrete colour palette for statesgetPalette
Data to produce plots of fitted modelgetPlotData
Wild haggis data set from Michelot et al. (2016, Methods Eco Evol)haggis_data
Is moveDatais.moveData
Is moveHMMis.moveHMM
Forward log-probabilitieslogAlpha
Backward log-probabilitieslogBeta
Constructor of 'moveData' objectsmoveData
Constructor of 'moveHMM' objectsmoveHMM
Scaling function: natural to working parameters.n2w
Negative log-likelihood functionnLogLike
Negative log-likelihoodnLogLike_rcpp
Parameters definitionparDef
Plot 'moveData'plot.moveData
Plot 'moveHMM'plot.moveHMM
Plot pseudo-residualsplotPR
Plot observations on satellite imageplotSat
Plot statesplotStates
Plot stationary state probabilitiesplotStationary
Predict stationary state probabilitiespredictStationary
Predict transition probabilities for new covariate valuespredictTPM
Preprocessing of the tracking dataprepData
Print 'moveHMM'print.moveHMM
Pseudo-residualspseudoRes
Sample from von Mises distributionrvm
Sample from wrapped Cauchy distributionrwrpcauchy
Simulation toolsimData
Split track at gapssplitAtGaps
State probabilitiesstateProbs
Stationary state probabilitiesstationary
Summary 'moveData'summary.moveData
Transition probability matrixtrMatrix_rcpp
Turning angleturnAngle
Viterbi algorithmviterbi
Scaling function: working to natural parametersw2n