`sparseLRMatrix`

provides a single matrix S4 class called `sparseLRMatrix`

which represents matrices that can be expressed as the sum of sparse matrix and a low rank matrix. We also provide an efficient SVD method for these matrices by wrapping the `RSpectra`

SVD implementation.

Eventually, we will fully subclass `Matrix::Matrix`

objects, but the current implementation is extremely minimal.

You can install the released version of sparseLRMatrix from CRAN with:

`install.packages("sparseLRMatrix")`

You can install the development version with:

```
# install.packages("remotes")
remotes::install_github("RoheLab/sparseLRMatrix")
```

```
library(sparseLRMatrix)
#> Loading required package: Matrix
library(RSpectra)
set.seed(528491)
n <- 50
m <- 40
k <- 3
A <- rsparsematrix(n, m, 0.1)
U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)
# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)
s <- svds(X, 5) # efficient
```

And a quick sanity check

```
Y <- A + tcrossprod(U, V)
s2 <- svds(Y, 5) # inefficient, but same calculation
# singular values match up, you can check for yourself
# that the singular vectors do as well!
all.equal(s$d, s2$d)
#> [1] TRUE
```