[r] clean up svds docs

r/NEWS.md

@@ -15,6 +15,7 @@ Contributions welcome :)
 - Speed up taking large subsets of large concatenated matrices, e.g. selecting 9M cells from a 10M cell matrix composed of ~100 concatenated pieces. (pull request #179)
 - `matrix_stats()` now also works with types `matrix` and `dgCMatrix`. (pull request #190)
 - Fixed memory errors when running `writeInsertionBed()` and `writeInsertionBedGraph()` (pull request #{118, 134})
+- Fix equation, parameter styling in `svds()`. (pull request #206)
 
 ## Bug-fixes
 - Fix error message printing when MACS crashes during `call_peaks_macs()` (pull request #175)

r/R/matrix-svds-docs.R

@@ -17,8 +17,7 @@
 #'
 #' @param A The matrix whose truncated SVD is to be computed.
 #' @param k Number of singular values requested.
-#' @param nu Number of left singular vectors to be computed. This must be between 0 and 'k'. (Must be equal to 'k' for BPCells IterableMatrix) 
-#' @param nu Number of right singular vectors to be computed. This must be between 0 and 'k'. (Must be equal to 'k' for BPCells IterableMatrix) 
+#' @param nu Number of right singular vectors to be computed. This must be between `0` and `k`. (Must be equal to `k` for BPCells IterableMatrix) 
 #' @param opts Control parameters related to computing algorithm. See *Details* below
 #' @param threads Control threads to use calculating mat-vec producs (BPCells specific)
 #' @return A list with the following components:
@@ -42,24 +41,24 @@
 #' \describe{
 #' \item{\code{ncv}}{Number of Lanzcos basis vectors to use. More vectors
 #'                   will result in faster convergence, but with greater
-#'                   memory use. \code{ncv} must be satisfy
-#'                   \eqn{k < ncv \le p}{k < ncv <= p} where
+#'                   memory use. \code{ncv} must satisfy
+#'                   \code{k < ncv <= p} where
 #'                   \code{p = min(m, n)}.
 #'                   Default is \code{min(p, max(2*k+1, 20))}.}
-#' \item{\code{tol}}{Precision parameter. Default is 1e-10.}
-#' \item{\code{maxitr}}{Maximum number of iterations. Default is 1000.}
+#' \item{\code{tol}}{Precision parameter. Default is `1e-10`.}
+#' \item{\code{maxitr}}{Maximum number of iterations. Default is `1000`.}
 #' \item{\code{center}}{Either a logical value (\code{TRUE}/\code{FALSE}), or a numeric
-#'                      vector of length \eqn{n}. If a vector \eqn{c} is supplied, then
-#'                      SVD is computed on the matrix \eqn{A - 1c'}{A - 1 * c'},
+#'                      vector of length `n`. If a vector `c` is supplied, then
+#'                      SVD is computed on the matrix `A-1c'`,
 #'                      in an implicit way without actually forming this matrix.
 #'                      \code{center = TRUE} has the same effect as
 #'                      \code{center = colMeans(A)}. Default is \code{FALSE}. Ignored in BPCells}
 #' \item{\code{scale}}{Either a logical value (\code{TRUE}/\code{FALSE}), or a numeric
-#'                     vector of length \eqn{n}. If a vector \eqn{s} is supplied, then
-#'                     SVD is computed on the matrix \eqn{(A - 1c')S}{(A - 1 * c')S},
-#'                     where \eqn{c} is the centering vector and \eqn{S = diag(1/s)}.
-#'                     If \code{scale = TRUE}, then the vector \eqn{s} is computed as
-#'                     the column norm of \eqn{A - 1c'}{A - 1 * c'}.
+#'                     vector of length `n`. If a vector `s` is supplied, then
+#'                     SVD is computed on the matrix `(A-1c')S`,
+#'                     where `c` is the centering vector and `S = diag(1/s)`.
+#'                     If \code{scale = TRUE}, then the vector `s` is computed as
+#'                     the column norm of `A - 1c'`.
 #'                     Default is \code{FALSE}. Ignored in BPCells}
 #' }
 #' @references Qiu Y, Mei J (2022). _RSpectra: Solvers for Large-Scale Eigenvalue and SVD Problems_. R package version 0.16-1, <https://CRAN.R-project.org/package=RSpectra>.