In TheoMichelot/hmmTMB: Fit Hidden Markov Models using Template Model Builder

This document lists vignettes and other resources for the R package hmmTMB, which include details about its implementation, as well as several step-by-step analyses on various types of time series data. The vignettes are available on Github, rather than directly included in the CRAN version of the package, because generating them takes a long time.

Preprint

A description of the statistical background, implementation details, and several detailed examples, are provided in the following arXiv preprint: 'hmmTMB: hidden Markov models with flexible covariate effects in R' (@michelot2022).

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 @kery2013.