The aim of the matlib package is pedagogical --- to help teach concepts in linear algebra, matrix algebra, and vector geometry that are useful in statistics. To this end, the package includes various functions for numerical linear algebra, most of which duplicate capabilities available elsewhere in R, but which are programmed transparently and purely in R code, including functions for solving possibly over- or under-determined linear simultaneous equations, for computing ordinary and generalized matrix inverses, and for producing various matrix decompositions. Many of these methods are implemented via Gaussian elimination. This paper focuses on the visualization facilities in the matlib package, including for graphing the solution of linear simultaneous equations in 2 and 3 dimensions; for demonstrating vector geometry in 2 and 3 dimensions; and for displaying the vector geometry of least-squares regression. We illustrate how these visualizations help to communicate fundamental ideas in linear algebra, vector geometry, and statistics. The 3D visualizations are implemented using the rgl package.
Karthik Ram is a co-founder of ROpenSci, and a data science fellow at the University of California's Berkeley Institute for Data Science. Karthik primarily works on a project that develops R-based tools to facilitate open science and access to open data.
