Imports: HiddenMarkov, mclust, mvtnorm, stats, MCMCpack, Rcpp (>=

Title: Improved Analysis of Marine Animal Movement Data Using Hidden
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.')
Description: 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.
Imports: dplyr, fields, foreach, imager, locfit, lubridate, maptools,
raster, RColorBrewer, rgeos, RNetCDF, sp, parallel, doParallel,
Benjamin Jones [ctb] (Contributed to earlier version of some of the
Martin Pedersen [ctb] (Developed an earlier version of some of the HMM

Title: Analysing Accelerometer Data Using Hidden Markov Models
Description: 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,

Title: Analysing accelerometer data using hidden Markov models
Author: Vitali Witowski, Dr. Ronja Foraita
Description: Analysing time-series accelerometer data to quantify length and intensity of physical activity.

Title: Hypothesis Testing and Power Calculations for Comparing
Author: Patricio S. La Rosa, Elena Deych, Berkley Shands, William D. Shannon
Depends: R (>= 2.10.0), lattice, MCMCpack, maxLik, binomSamSize, Hmisc,
Description: This package provides several functions to perform formal hypothesis testing, and power and sample size calculations for human microbiome experiments. Dirichlet-Multinomial distribution is proposed for modeling the species abundance distributions of human microbiota (ranked bacterial taxa counts).