Ready for other members of lab

Author: alexpghayes

.Rbuildignore

@@ -2,3 +2,6 @@
 ^\.Rproj\.user$
 ^LICENSE\.md$
 ^README\.Rmd$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$

.gitignore

@@ -1,3 +1,4 @@
 .Rproj.user
 .Rhistory
 .RData
+docs

NAMESPACE

@@ -2,7 +2,5 @@
 
 S3method(svds,sparseLRMatrix)
 export(sparseLRMatrix)
-export(svds)
 import(Matrix)
 importFrom(RSpectra,svds)
-importFrom(methods,new)

R/object.R

@@ -1,3 +1,6 @@
+#' @import Matrix
+NULL
+
 #' Sparse plus low rank matrix
 #'
 #' Eventually this class will subclass `Matrix` objects,
@@ -26,19 +29,7 @@ setClass(
   )
 )
 
-#' Title
-#'
-#' @param sparse TODO
-#' @param U TODO
-#' @param V TODO
-#'
-#' @return
 #' @export
-#' @importFrom methods new
-#'
-#' @examples
-#'
-#' # TODO
 sparseLRMatrix <- function(sparse, U, V) {
   methods::new(
     Class = "sparseLRMatrix",
@@ -72,10 +63,6 @@ Atx <- function(x, A) {
   drop(out)
 }
 
-#' @export
-#' @importFrom RSpectra svds
-svds <- RSpectra::svds
-
 #' Truncated singular value decomposition of a matrix
 #'
 #' A thin wrapper around [RSpectra::svds()], please see more detailed
@@ -89,7 +76,9 @@ svds <- RSpectra::svds
 #' @param opts Passed to [RSpectra::svds()].
 #' @param ... Passed to [RSpectra::svds()].
 #'
+#' @importFrom RSpectra svds
 #' @method svds sparseLRMatrix
+#'
 #' @export
 #'
 #' @examples
@@ -110,7 +99,7 @@ svds <- RSpectra::svds
 #' svds(X, 5)
 #'
 svds.sparseLRMatrix <- function(A, k, nu = k, nv = k, opts = list(), ...) {
-  svds(
+  RSpectra::svds(
     Ax,
     k,
     nu = nu,

R/sparseLRMatrix-package.R

@@ -16,39 +16,53 @@ knitr::opts_chunk$set(
 # sparseLRMatrix
 
 <!-- badges: start -->
+[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 <!-- badges: end -->
 
-The goal of sparseLRMatrix is to ...
+`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("sparseLRMatrix")
+# install.packages("remotes")
+remotes::install_github("RoheLab/sparseLRMatrix")
 ```
 
-## Example
-
-This is a basic example which shows you how to solve a common problem:
+## Usage
 
 ```{r example}
 library(sparseLRMatrix)
-## basic example code
-```
+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)
 
-What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:
+# construct the matrix, which represents A + U %*% t(V)
+X <- sparseLRMatrix(sparse = A, U = U, V = V)
 
-```{r cars}
-summary(cars)
+s <- svds(X, 5)  # efficient
 ```
 
-You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date.
+And a quick sanity check
 
-You can also embed plots, for example:
+```{r}
+Y <- A + tcrossprod(U, V)
+s2 <- svds(Y, 5)  # inefficient, but same calculation
 
-```{r pressure, echo = FALSE}
-plot(pressure)
+# singular values match up, you can check for yourself
+# that the singular vectors do as well!
+all.equal(s$d, s2$d)
 ```
 
-In that case, don't forget to commit and push the resulting figure files, so they display on GitHub!

README.Rmd

@@ -0,0 +1,65 @@
+
+<!-- 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
+```