Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.
|Author||Roger Koenker <firstname.lastname@example.org> and Pin Ng <Pin.Ng@NAU.EDU>|
|Date of publication||2016-11-10 17:09:29|
|Maintainer||Roger Koenker <email@example.com>|
|License||GPL (>= 2)|
character-null-class: Class "character or NULL"
lsq: Least Squares Problems in Surveying
matrix.coo-class: Class "matrix.coo"
matrix.csc-class: Class "matrix.csc"
matrix.csc.hb-class: Class "matrix.csc.hb"
matrix.csr.chol-class: Class "matrix.csr.chol"
matrix.csr-class: Class "matrix.csr"
matrix.ssc-class: Class "matrix.ssc"
matrix.ssc.hb-class: Class "matrix.ssc.hb"
matrix.ssr-class: Class "matrix.ssr"
mslm-class: Class "mslm"
numeric-null-class: Class "numeric or NULL"
slm: Fit a linear regression model using sparse matrix algebra
slm-class: Class "slm"
slm.fit: Internal slm fitting functions
slm.methods: Methods for slm objects
SparseM.hb: Harwell-Boeing Format Sparse Matrices
SparseM.image: Image Plot for Sparse Matrices
SparseM.ontology: Sparse Matrix Class
SparseM.ops: Basic Linear Algebra for Sparse Matrices
SparseM.solve: Linear Equation Solving for Sparse Matrices
summary.mslm-class: Class "summary.mslm"
summary.slm-class: Class "summary.slm"
triogramX: A Design Matrix for a Triogram Problem