https://brettmcclintock-noaa.r-universe.devRecent package updates in brettmcclintock-noaaR-universehttps://github.com/brettmcclintock-noaa.pnghttps://brettmcclintock-noaa.r-universe.devThu, 28 May 2026 22:18:43 GMTbrett.mcclintock@noaa.gov (Brett McClintock)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, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion 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>.https://github.com/r-universe/bmcclintock/actions/runs/38243859372Thu, 28 May 2026 22:18:43 GMTmomentuHMM2.0.0successhttps://bmcclintock.r-universe.devhttps://github.com/bmcclintock/momentuHMMmomentuHMM.pdf.asismomentuHMM.pdfGuide to using momentuHMM2019-12-17 21:02:122019-12-17 21:02:12brett.mcclintock@noaa.gov (Brett T. McClintock)Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.https://github.com/r-universe/bmcclintock/actions/runs/39236121465Tue, 25 Nov 2025 04:28:49 GMTmultimark2.1.7successhttps://bmcclintock.r-universe.devhttps://github.com/bmcclintock/multimark