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>.

Tracks elapsed clock time using a `hms::hms()` scalar, which
    if running has an attribute named start that specifies the system time
    when the timer was started.  The elapsed time is the value of the
    scalar plus the difference between the current system time and the
    system time when the timer was started.

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.

Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.