Functionality for manipulating values of associative
  maps.  The package is a dependency for mvp-type
  packages that use the STL map class: it traps
  plausible idiom that is ill-defined (implementation-specific) and
  returns an informative error, rather than returning a possibly
  incorrect result.  To cite the package in publications please use
  Hankin (2022) <doi:10.48550/ARXIV.2210.03856>.

Simulates demic diffusion building on models previously developed
	for the expansion of Neolithic and other food-producing economies during
	the Holocene (Fort et al. (2012) <doi:10.7183/0002-7316.77.2.203>, Souza et al.
	(2021) <doi:10.1098/rsif.2021.0499>). Growth and emigration are modelled as
	density-dependent processes using logistic growth and an asymptotic threshold
	model. Environmental and terrain layers, which can change over time, affect
	carrying capacity, growth and mobility. Multiple centres of origin with
	their respective starting times can be specified.

Provides methods for analyzing the dispersion of tabular datasets
    with batched and ordered samples. Based on convex hull or integrated covariance
    Mahalanobis, several indicators are implemented for inter and intra batch dispersion
    analysis. It is designed to facilitate robust statistical assessment of data
    variability, supporting applications in exploratory data analysis and quality
    control, for such datasets as the one found in metabololomics studies.
    For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and
    Salanon (2025) <doi:10.1101/2025.08.01.668073>.

When visualising changes between two values over time, a strict
 linear interpolation can look jarring and unnatural.  By applying a non-linear
 easing to the transition, the motion between values can appear smoother and 
 more natural.  This package includes functions for applying such non-linear
 easings to colors and numeric values, and is useful where smooth animated movement
 and transitions are desired.

Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach.