A comprehensive R interface to access data from the Kraken cryptocurrency exchange REST API <https://docs.kraken.com/api/>.
  It allows users to retrieve various market data, such as asset information, trading pairs, and price data.
  The package is designed to facilitate efficient data access for analysis, strategy development, and monitoring of cryptocurrency market trends.

Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>)
    for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and
    uses this information to place products and services within the Kraljic Matrix.

Provides basic functions for Continuation-Passing Style development.

Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.

An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data.

Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.

Provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. Both the customary methodology and Hughes' methodology <DOI:10.48550/arXiv.2210.13265> are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology.

Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.