Title: Distributions, Statistics and Tests derived from Random Matrix
Author: Iain M. Johnstone, Zongming Ma, Patrick O. Perry and Morteza

Title: Distributions, Statistics and Tests Derived from Random Matrix
Author: Iain M. Johnstone, Zongming Ma, Patrick O. Perry, Morteza Shahram, and Evan Biederstedt
Functions for working with the Tracy-Widom laws and other distributions
related to the eigenvalues of large Wishart matrices.
functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram.
Packaged: 2022-04-12 20:40:45 UTC; evanbiederstedt

Author: Iain M. Johnstone, Zongming Ma, Patrick O. Perry and Morteza Shahram.
Maintainer: Patrick O. Perry <pperry@stern.nyu.edu>
functions were computed by Momar Dieng's MATLAB package "RMLab"
Zongming Ma, Patrick Perry, and Morteza Shahram.  It will soon be
replaced by a package with more accuracy and built-in support for

Title: Mappable Vector Library for Handling Large Datasets
comment = structure("https://orcid.org/0000-0003-4708-6625", .Names = "ORCID"))
Description: Mappable vector library provides convenient way to access large datasets on solid state drives. This bypasses limitation of physical memory size as well as limited bandwidth of database interfaces. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface. The data is memory mapped into R and then accessed using usual R list and array subscription operators. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as standalone C library. libMVL has simple C API making it easy to interchange of datasets with outside programs.
Author: Vladimir Dergachev [aut, cre] (<https://orcid.org/0000-0003-4708-6625>)

Description: Mappable vector library provides convenient way to access large datasets on solid state drives. This bypasses limitation of physical memory size as well as limited bandwidth of database interfaces. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface. The data is memory mapped into R and then accessed using usual R list and array subscription operators. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as standalone C library. libMVL has simple C API making it easy to interchange of datasets with outside programs.