Description: 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

Title: Fast Computation of some Matrices Useful in Statistics
Description: 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 the GCV procedure), functions to compute
the multivariate skewness, kurtosis, Mahalanobis distance (checking the positive
defineteness) and the Wilson-Hilferty transformation of chi squared variables. Furthermore,
the package provides interfaces to C code callable by another C code from other R packages.

selection of the ridge parameter considering several procedures), functions to compute

Title: Fast Mutual Information Based Independence Test
person("Shiyun", "Lin", , "linshy27@mail2.sysu.edu.cn", c("aut", "cre")),
person("Jin", "Zhu", , "zhuj37@mail2.sysu.edu.cn", "aut"),
person("Wenliang", "Pan", role = "aut"),
person("Xueqin", "Wang", , "wangxq88@mail.sysu.edu.cn", role = "aut"),
Description: 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.