R: Parallel Low-Rank Approximation with Nonnegativity Constraints

# Parallel Low-Rank Approximation with Nonnegativity Constraints

## Documentation for package ‘RcppPlanc’ version 2.0.15

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

as.H5Mat | Argument list object for using a dense matrix stored in HDF5 file
as.H5Mat.default | Argument list object for using a dense matrix stored in HDF5 file
as.H5Mat.dgCMatrix | Argument list object for using a dense matrix stored in HDF5 file
as.H5Mat.matrix | Argument list object for using a dense matrix stored in HDF5 file
as.H5SpMat | Argument list object for using a sparse matrix stored in HDF5 file
as.H5SpMat.default | Argument list object for using a sparse matrix stored in HDF5 file
as.H5SpMat.dgCMatrix | Argument list object for using a sparse matrix stored in HDF5 file
as.H5SpMat.matrix | Argument list object for using a sparse matrix stored in HDF5 file
bppnnls | Block Principal Pivoted Non-Negative Least Squares
bppnnls_prod | Block Principal Pivoted Non-Negative Least Squares
ctrl.sparse | Example single-cell transcriptomic data in sparse form
dim.H5SpMat | Retrieve the dimension of H5SpMat argument list
dim<-.H5SpMat | Retrieve the dimension of H5SpMat argument list
format.H5Mat | Prepare character information of a H5Mat object
format.H5SpMat | prepare character information of a H5SpMat object
H5Mat | Argument list object for using a dense matrix stored in HDF5 file
H5SpMat | Argument list object for using a sparse matrix stored in HDF5 file
inmf | Perform Integrative Non-negative Matrix Factorization
nmf | Perform Non-negative Matrix Factorization
onlineINMF | Perform Integrative Non-negative Matrix Factorization Using Online Learning
print.H5Mat | Show information of a H5Mat object
print.H5SpMat | Show information of a H5SpMat object
stim.sparse | Example single-cell transcriptomic data in sparse form
symNMF | Perform Symmetric Non-negative Matrix Factorization
uinmf | Perform Mosaic Integrative Non-negative Matrix Factorization with Unshared Features