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.

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>.

Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", <https://depmix.github.io/hmmr/>.

Fitting hidden Markov models using automatic differentiation 
  and Laplace approximation, allowing for fast inference and flexible covariate
  effects (including random effects and smoothing splines) on model parameters.
  The package is described by Michelot (2022) <arXiv:2211.14139>.

Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.

Tools to model, compare, and visualize populations of taxonomic tree objects.

Statistical analysis of static chamber concentration data for trace gas flux estimation.

Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.

Hierarchical Modelling of Species Communities (HMSC) is
   a model-based approach for analyzing community ecological data. 
   This package implements it in the Bayesian framework with Gibbs
   Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020)
   <doi:10.1111/2041-210X.13345>).

The HMS (Hierarchic Memetic Strategy) is a composite global optimization
  strategy consisting of a multi-population evolutionary strategy and some
  auxiliary methods. The HMS makes use of a dynamically-evolving data structure
  that provides an organization among the component populations. It is a tree
  with a fixed maximal height and variable internal node degree. Each component
  population is governed by a particular evolutionary engine. This package
  provides a simple R implementation with examples of using different genetic
  algorithms as the population engines. References: J. Sawicki, M. Łoś,
  M. Smołka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.