Fast implementation of data structures, including a key-value
    store, stack, and queue. Environments are commonly used as key-value stores
    in R, but every time a new key is used, it is added to R's global symbol
    table, causing a small amount of memory leakage. This can be problematic in
    cases where many different keys are used. Fastmap avoids this memory leak
    issue by implementing the map using data structures in C++.

Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.

Small set of functions to fast computation of some matrices and operations
  useful in statistics and econometrics. Currently, there are functions for efficient
  computation of duplication, commutation and symmetrizer matrices with minimal storage
  requirements. Some commonly used matrix decompositions (LU and LDL), basic matrix
  operations (for instance, Hadamard, Kronecker products and the Sherman-Morrison formula)
  and iterative solvers for linear systems are also available. In addition, the package
  includes a number of common statistical procedures such as the sweep operator, weighted
  mean and covariance matrix using an online algorithm, linear regression (using Cholesky,
  QR, SVD, sweep operator and conjugate gradients methods), ridge regression (with optimal
  selection of the ridge parameter considering several procedures), omnibus tests for 
  univariate normality, functions to compute the multivariate skewness, kurtosis, the 
  Mahalanobis distance (checking the positive defineteness), and the Wilson-Hilferty 
  transformation of gamma variables. Furthermore, the package provides interfaces 
  to C code callable by another C code from other R packages.

A mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.