The HMMmix (HMM with mixture of gaussians as emission
distribution) package
This package allows one to reduce a K-states HMM to a
D-states HMM, with D < K.

Determine the Number of States in Hidden Markov Models via
Marginal Likelihood
Yang Chen, Cheng-Der Fuh, Chu-Lan Kao, and S. C. Kou.
Provide functions to make estimate the number of states for a hidden Markov
model (HMM) using marginal likelihood method proposed by the authors.
See the Manual.pdf file a detail description of all functions, and a detail tutorial.
HiddenMarkov, mclust, mvtnorm, stats, DirichletReg, Rcpp (>=
0.12.10)

HiddenMarkov, mclust, mvtnorm, stats, MCMCpack, Rcpp (>=
0.12.10)

Improved Analysis of Marine Animal Movement Data Using Hidden
Markov Models
c(
person("Camrin", "Braun", email = "camrin.braun@gmail.com", role = c("aut","cre")),
person("Benjamin", "Galuardi", email = "drdrumfish@gmail.com", role = c("aut")),
person("Benjamin", "Jones", role = c("ctb"), comment = 'Contributed to earlier version of some of the download functions.'),
person("Martin", "Pedersen", role = c("ctb"), comment = 'Developed an earlier version of some of the HMM framework and helper functions.')
)
Improved analysis of marine animal movement data by implementing
a state-space hidden Markov model (HMM) to improve position estimates. Position
estimates are derived by comparing electronic tag data (from tags deployed on
marine animals, typically fish) to three-dimensional oceanographic data.
dplyr, fields, foreach, imager, locfit, lubridate, maptools,
raster, RColorBrewer, rgeos, RNetCDF, sp, parallel, doParallel,
curl, methods
Camrin Braun [aut, cre],
Benjamin Galuardi [aut],
Benjamin Jones [ctb] (Contributed to earlier version of some of the
download functions.),
Martin Pedersen [ctb] (Developed an earlier version of some of the HMM
framework and helper functions.)

Analysing Accelerometer Data Using Hidden Markov Models
Analysing time-series accelerometer data to quantify length and
intensity of physical activity using hidden Markov models.
It also contains the traditional cut-off point method.
Witowski V, Foraita R, Pitsiladis Y, Pigeot I,
Wirsik N (2014)<doi:10.1371/journal.pone.0114089>.