An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.

Apply an adaptation of the SuperFastHash algorithm to any R
    object. Hash whole R objects or, for vectors or lists, hash R objects to obtain
    a set of hash values that is stored in a structure equivalent to the input. See
    <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash
    algorithm.

The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. 
    The  Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for 
    any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to 
    the number of lags involved in the test and therefore it should be used with extra caution.
    The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.

A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) 
  test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve
  two distinct purposes. Initially, the test seeks to determine whether there exists a 
  statistically significant difference between the distribution of forecast errors, and 
  secondly it exploits the principles of stochastic dominance to determine whether the 
  forecasts with the lower error also reports a stochastically smaller error than forecasts 
  from a competing model, and thereby enables distinguishing between the predictive accuracy 
  of forecasts. KSPA test has been described in : Hassani and Silva (2015) <doi:10.3390/econometrics3030590>.