hmmTMB

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.

Preprint

The statistical background, as well as details about the implementation of the package, and several example analyses, are presented in the following preprint.

Michelot, T. (2022). hmmTMB: Hidden Markov models with flexible covariate effects in R. arXiv:2211.14139.

Package installation

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")

Package documentation

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
?HMM

We 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).