The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This R package implements flexible hidden Markov models, based on Template Model Builder (TMB): flexible state-dependent distributions, transition probability structures, random effects, and smoothing splines.
The statistical background, as well as details about the implementation of the package, and several example analyses, are presented in the following preprint.
The package is available on CRAN, and the stable version can therefore be installed using
install.packages("hmmTMB")The development version of the package can be installed from Github using devtools,
devtools::install_github("TheoMichelot/hmmTMB")To find help files for the methods implemented in the package, search for help using the name of the corresponding class, e.g.,
?MarkovChain
?Observation
?HMMWe describe functionalities of the package in several vignettes:
‘Analysing time series data with hidden Markov models in hmmTMB’: Overview of package workflow, using detailed example based on analysis of energy prices. This is a good starting point to learn how to use the package.
‘Bayesian inference in hmmTMB’: Description of workflow for Bayesian analysis in hmmTMB, including specifying priors, and extracting posterior samples.
‘Advanced features of hmmTMB’: Description of some other useful functionalities, including (semi-)supervised learning, parameter constraints, selection of initial parameter values, etc.
‘General dependence structures in hmmTMB’: Implementation details for hidden Markov models (HMMs) with non-standard dependence structures, including hidden semi-Markov models, higher-order HMMs, autoregressive HMMs, and coupled HMMs.
‘List of distributions in hmmTMB’: List of observation distributions currently available in hmmTMB.
‘Flexible animal movement modelling using hmmTMB’: Description of wild haggis movement analysis, illustrating how non-parametric covariate effects can be included. This includes two different types of movement models: (1) correlated random walks based on step lengths and turning angles, and (2) correlated random walks based on locations directly.
‘Occupancy modelling using hmmTMB’: Analysis of occupancy data set of crossbill from Kéry et al. (2013).
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.