Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel, and 'Rcpp'
 has been used to speed up calculations.
 For more info about Gaussian process software, see Erickson et al. (2018)
 <doi:10.1016/j.ejor.2017.10.002>.

A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. 'gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence' in the journal Ecology and Evolution.

A collection difference measures for multivariate Gaussian
        probability density functions, such as the Euclidea mean, the
        Mahalanobis distance, the Kullback-Leibler divergence, the
        J-Coefficient, the Minkowski L2-distance, the Chi-square
        divergence and the Hellinger Coefficient.

Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.