Package: moveHMM 1.9

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, Roland Langrock, Toby Patterson, Brett McClintock, Eric Rexstad

moveHMM_1.9.tar.gz
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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

5.33 score 108 scripts 717 downloads 22 mentions 16 exports 56 dependencies

Last updated 2 years agofrom:7ecd2892ef. Checks:OK: 1 WARNING: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linux-x86_64WARNINGNov 21 2024

Exports:CIfitHMMgetPlotDatanLogLikeplotPRplotSatplotStatesplotStationarypredictStationarypredictTPMprepDatapseudoRessimDatastateProbsstationaryviterbi

Dependencies:askpassbitopsbootCircStatsclicolorspacecpp11curldigestdplyrfansifarvergenericsgeosphereggmapggplot2gluegtablehttrisobandjpegjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmimemunsellnlmenumDerivopensslpillarpkgconfigplyrpngpurrrR6RColorBrewerRcppRcppArmadillorlangscalesspstringistringrsystibbletidyrtidyselectutf8vctrsviridisLitewithr

Choosing starting values in moveHMM

Rendered frommoveHMM-starting-values.Rnwusingknitr::knitron Nov 21 2024.

Last update: 2023-05-08
Started: 2019-05-19

Custom plots with moveHMM

Rendered frommoveHMM-custom-plots.Rnwusingknitr::knitron Nov 21 2024.

Last update: 2019-05-19
Started: 2019-05-19

Guide to using moveHMM

Rendered frommoveHMM-guide.Rnwusingknitr::knitron Nov 21 2024.

Last update: 2022-05-13
Started: 2015-12-03

moveHMM workflow: wild haggis analysis

Théo Michelot

Rendered frommoveHMM-example.Rmdusingknitr::rmarkdownon Nov 21 2024.

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

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
Von Mises density functiondvm_rcpp
Weibull density functiondweibull_rcpp
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
Simulation toolsimData
State probabilitiesstateProbs
Stationary state probabilitiesstationary
Summary 'moveData'summary.moveData
Transition probability matrixtrMatrix_rcpp
Turning angleturnAngle
Viterbi algorithmviterbi
Scaling function: working to natural parametersw2n