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Fitting hidden Markov models using automatic differentiation and Laplace approximation, allowing for fast inference and flexible covariate effects (including random effects and smoothing splines) on model parameters. The package is described by Michelot (2022) <doi:10.48550/arXiv.2211.14139>.
Version: | 1.0.2 |
Depends: | R6, mgcv, TMB, ggplot2 |
Imports: | Matrix, stringr, optimx, CircStats, MASS, tmbstan, methods |
LinkingTo: | TMB, RcppEigen |
Suggests: | rstan, testthat, knitr, moveHMM, scico, MSwM, unmarked |
Published: | 2023-10-24 |
DOI: | 10.32614/CRAN.package.hmmTMB |
Author: | Theo Michelot [aut, cre], Richard Glennie [aut, ctb] |
Maintainer: | Theo Michelot <theo.michelot at dal.ca> |
License: | GPL-3 |
URL: | https://github.com/TheoMichelot/hmmTMB |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | hmmTMB results |
Reference manual: | hmmTMB.pdf |
Vignettes: |
Online resources for hmmTMB |
Package source: | hmmTMB_1.0.2.tar.gz |
Windows binaries: | r-devel: hmmTMB_1.0.2.zip, r-release: hmmTMB_1.0.2.zip, r-oldrel: hmmTMB_1.0.2.zip |
macOS binaries: | r-release (arm64): hmmTMB_1.0.2.tgz, r-oldrel (arm64): hmmTMB_1.0.2.tgz, r-release (x86_64): hmmTMB_1.0.2.tgz, r-oldrel (x86_64): hmmTMB_1.0.2.tgz |
Old sources: | hmmTMB archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.