All data sets required for the examples and exercises
  in the book "Forecasting: principles and practice" (2nd ed, 2018)
  by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>.
  All packages required to run the examples are also loaded.

All data sets required for the examples and exercises in the book
    "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos
    <https://OTexts.com/fpp3/>. All packages required to run the examples are also
    loaded. Additional data sets not used in the book are also included.

Provides optimized 'C++' code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) <doi:10.1016/j.ecolmodel.2007.11.008>. Parallelization via 'OpenMP' was implemented with assistance from the 'DeepSeek' Artificial Intelligence Assistant (<https://www.deepseek.com/>).

Provides functions for calculating various measures of foreign 
    policy similarity or association commonly used in the study of international 
    relations. These include Signorino and Ritter's S statistic (weighted and 
    unweighted), Cohen's weighted kappa, Scott's pi, and Kendall's tau-b. The 
    package facilitates the generation of dyadic similarity scores for 
    empirical analyses and can also serve as an educational resource for 
    understanding how such measures are derived.

Efficient approximation of first passage time densities for diffusion processes based on the First Passage Time Location (FPTL) function.

The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.