lazymatrix 0.1.0
Initial CRAN submission.
New Features
- Introduced lazy evaluation framework for working with normalized
sparse matrices
- Represent large sparse matrices symbolically with the S4 object
LazyMatrix
- Fundamental matrix operations like matrix-vector multiplication and
transpose matrix-vector multiplication supported for the
LazyMatrix object
- Added support for subsetting methods, mirroring matrix objects from
base R and
Matrix. The symbolic equivalent of column
vectors is called LazyColumn
- Implemented specific statistical algorithms, LSQR, truncated partial
SVD, for showcasing how
lazymatrix can be implemented
- Optimized internal C++ loops for sparse matrix operations using
RcppArmadillo.
Improvements
- Optimized matrix multiplication for lazy objects to avoid
materialization
- Added vignette demonstrating typical workflow with large sparse
datasets
Bug Fixes
- No known bugs fixed in initial release
Documentation
- Added comprehensive
roxygen2 documentation for all
exported functions
- Included example code in each function’s documentation
- Wrote getting started vignette covering installation and basic
usage
Internal Changes
- Utilized Matrix package for underlying sparse matrix
representation
- Implemented partial SVD using
irlba