A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.

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 (<doi:10.1007/978-3-319-19219-2>) 
  is a field survey and analytical method that estimates density and 
  abundance of survey targets (e.g., animals) when 
  detection probability declines with observation distance. 
  Distance-sampling is popular in ecology, 
  especially when survey targets are observed from aerial platforms (e.g., 
  airplane or drone), surface vessels (e.g., boat or truck), or along 
  walking transects. Analysis involves fitting smooth (parametric) curves to 
  histograms of observation distances and using those functions to 
  adjust density estimates for missed targets.  Routines included here 
  fit curves to observation distance histograms, estimate effective 
  sampling area, density of targets in surveyed areas, and the abundance 
  of targets in a surrounding study area. Confidence interval estimation
  uses built-in bootstrap resampling. Help files are extensive and have been 
  vetted by multiple authors. Many tutorials are available on the package's 
  website (URL below).