The 'RDieHarder' package provides an R interface to 
 the 'DieHarder' suite of random number generators and tests that 
 was developed by Robert G. Brown and David Bauer, extending 
 earlier work by George Marsaglia and others. The 'DieHarder'
 library code is included.

We provide linear and nonlinear dimension reduction techniques.
	Intrinsic dimension estimation methods for exploratory analysis are also provided.
	For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

A common framework for calculating distance matrices.

Distance-sampling analyses estimate density and 
  abundance of organisms in ecology when detection probability declines with 
  increasing distance from the observers. Distance-sampling is popular 
  in most branches of ecology and especially when 
  organisms are observed from aerial platforms (e.g., airplane or drone), 
  surface vessels (e.g., boat or truck), or along walking transects. 
  Rdistance analyzes data collected on both point and line transects, 
  estimates overall (study area) and site-level (transect or point) 
  density, and allows users to specify regression-like formula (similar 
  to lm or glm) for covariates. A large suite of classical, 
  parametric detection functions are 
  included along with some uncommon parametric 
  functions (e.g., Gamma, negative exponential) and non-parametric
  smoothed distance functions. Custom (user-defined) detection functions
  can be implemented (see vignette).  Measurement unit integrity is 
  enforced with internal unit conversion when necessary. 
  The help files and vignettes have been 
  vetted by multiple authors and tested in workshop 
  settings.

Provides a framework for the measurement and partitioning of
    the (similarity-sensitive) biodiversity of a metacommunity and its
    constituent subcommunities. Richard Reeve, et al. (2016) 
    <arXiv:1404.6520v3>.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.