Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models R package momentuHMM version 1.5.8 CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) CRAN Task View: SpatioTemporal (https://CRAN.R-project.org/view=SpatioTemporal) CRAN Task View: Tracking (https://CRAN.R-project.org/view=Tracking) Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>. Software Depends: R (>= 2.10) Imports: Rcpp, doParallel, foreach, numDeriv, CircStats, crawl (>= 2.2.1), mvtnorm, sp, MASS, Brobdingnag, doRNG, rlang, raster LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, setRNG, splines, splines2 (>= 0.2.8), R.rsp, conicfit, ggplot2, ggmap, lubridate, dplyr, magrittr, scatterplot3d, BB, expm, matrixcalc, moveHMM, extraDistr, data.tree (>= 1.0.0), geosphere, mitools, doFuture, future, car, survival, prodlim, nleqslv, qdapRegex Brett McClintock <brett.mcclintock@noaa.gov> Comprehensive R Archive Network (CRAN) Brett McClintock [aut, cre], Theo Michelot [aut] GPL-3 2025-11-25 application/tgz https://CRAN.R-project.org/package=momentuHMM doi:10.32614/CRAN.package.momentuHMM