moveHMM1.11 package

Animal Movement Modelling using Hidden Markov Models

AIC.moveHMM

AIC

allProbs

Matrix of all probabilities

angleCI

Confidence intervals for angle parameters

CI

Confidence intervals

dexp_rcpp

Exponential density function

dgamma_rcpp

Gamma density function

dlnorm_rcpp

Log-normal density function

dvm_rcpp

Von Mises density function

dvm

Density function of von Mises distribution

dweibull_rcpp

Weibull density function

dwrpcauchy_rcpp

Wrapped Cauchy density function

dwrpcauchy

Density function of wrapped Cauchy distribution

exGen

Example data simulation

fitHMM

Fit an HMM to the data

getPalette

Discrete colour palette for states

getPlotData

Data to produce plots of fitted model

is.moveData

Is moveData

is.moveHMM

Is moveHMM

logAlpha

Forward log-probabilities

logBeta

Backward log-probabilities

moveData

Constructor of moveData objects

moveHMM

Constructor of moveHMM objects

n2w

Scaling function: natural to working parameters.

nLogLike_rcpp

Negative log-likelihood

nLogLike

Negative log-likelihood function

parDef

Parameters definition

plot.moveData

Plot moveData

plot.moveHMM

Plot moveHMM

plotPR

Plot pseudo-residuals

plotSat

Plot observations on satellite image

plotStates

Plot states

plotStationary

Plot stationary state probabilities

predictStationary

Predict stationary state probabilities

predictTPM

Predict transition probabilities for new covariate values

prepData

Preprocessing of the tracking data

print.moveHMM

Print moveHMM

pseudoRes

Pseudo-residuals

rvm

Sample from von Mises distribution

rwrpcauchy

Sample from wrapped Cauchy distribution

simData

Simulation tool

splitAtGaps

Split track at gaps

stateProbs

State probabilities

stationary

Stationary state probabilities

summary.moveData

Summary moveData

trMatrix_rcpp

Transition probability matrix

turnAngle

Turning angle

viterbi

Viterbi algorithm

w2n

Scaling function: working to natural parameters

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>.

  • Maintainer: Theo Michelot
  • License: GPL-3
  • Last published: 2025-06-23