Compile README

Author: alexpghayes

README.md

@@ -1,65 +1,68 @@
-
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
-# sparseLRMatrix
-
-<!-- badges: start -->
-
-[![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
-<!-- badges: end -->
-
-`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.
-
-## Installation
-
-You can install the released version of sparseLRMatrix from
-[CRAN](https://CRAN.R-project.org) with:
-
-``` r
-# install.packages("remotes")
-remotes::install_github("RoheLab/sparseLRMatrix")
-```
-
-## Usage
-
-``` r
-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
-
-``` r
-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
-```
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# sparseLRMatrix
+
+<!-- badges: start -->
+
+[![Lifecycle:
+experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
+[![Codecov test
+coverage](https://codecov.io/gh/RoheLab/sparseLRMatrix/branch/master/graph/badge.svg)](https://codecov.io/gh/RoheLab/sparseLRMatrix?branch=master)
+[![R-CMD-check](https://github.com/RoheLab/sparseLRMatrix/workflows/R-CMD-check/badge.svg)](https://github.com/RoheLab/sparseLRMatrix/actions)
+<!-- badges: end -->
+
+`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.
+
+## Installation
+
+You can install the released version of sparseLRMatrix from
+[CRAN](https://CRAN.R-project.org) with:
+
+``` r
+# install.packages("remotes")
+remotes::install_github("RoheLab/sparseLRMatrix")
+```
+
+## Usage
+
+``` r
+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
+
+``` r
+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
+```