Returns dynamic fit index (DFI) cutoffs for latent variable models 
    that are tailored to the user's model statement, model type, and sample size.  
    This is the counterpart of the Shiny Application, <https://dynamicfit.app>.

Emulating and solving inverse problems for dynamic computer experiments.
	     It contains two major functionalities: (1) localized GP model for large-scale
	     dynamic computer experiments using the algorithm proposed by
	     Zhang et al. (2018) <arXiv:1611.09488>; (2) solving inverse problems
	     in dynamic computer experiments. The current version only supports 64-bit
	     version of R.

A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.

Contains methods for detection of clusters in hierarchical clustering dendrograms.

Easy-to-use and efficient interface for 
  Bayesian inference of complex panel (time series) data using dynamic 
  multivariate panel models by Helske and Tikka (2022) 
  <doi:10.31235/osf.io/mdwu5>. The package supports joint modeling of multiple 
  measurements per individual, time-varying and time-invariant effects, and a 
  wide range of discrete and continuous distributions. Estimation of these 
  dynamic multivariate panel models is carried out via 'Stan'. For an 
  in-depth tutorial of the package, see (Tikka and Helske, 2023) 
  <arxiv:2302.01607>.

Provides functions for inferring longitudinal dominance hierarchies, which describe dominance relationships and their dynamics in a single latent hierarchy over time. Strauss & Holekamp (in press).