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 (2024) 
  <doi:10.1016/j.alcr.2024.100617>. 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, 2025) <doi:10.18637/jss.v115.i05>.

It allows running 'Dynare' program from base R, R Markdown and Quarto. 'Dynare' is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium ('DSGE') and overlapping generations ('OLG') models.  This package does not only integrate R and Dynare but also serves as a 'Dynare' Knit-Engine for 'knitr' package. The package requires 'Dynare' (<https://www.dynare.org/>) and 'Octave' (<https://www.octave.org/download.html>).  Write all your 'Dynare' commands in R or R Markdown chunk.

Implements the quantile-graph based Dynamics Similarity Coefficient (DSC) for comparing intrinsic dynamics of time series.

A set of tools to generate dynamic spectrogram visualizations in video format.