R/coop-package.r

In coop: Co-Operation: Fast Covariance, Correlation, and Cosine Similarity Operations

#' Cooperation: A Package of Co-Operations
#' 
#' @description
#' Fast implementations of the co-operations: covariance,
#' correlation, and cosine similarity.  The implementations are
#' fast and memory-efficient and their use is resolved
#' automatically based on the input data, handled by R's S3
#' methods.  Full descriptions of the algorithms and benchmarks
#' are available in the package vignettes.
#' 
#' Covariance and correlation should largely need no introduction.
#' Cosine similarity is commonly needed in, for example, natural
#' language processing, where the cosine similarity coefficients
#' of all columns of a term-document or document-term matrix is
#' needed.
#' 
#' @section The \code{inplace} argument:
#' When computing covariance and correlation with dense matrices, 
#' we must operate on the centered and/or scaled input data.  When
#' \code{inplace=FALSE}, a copy of the matrix is made.  This
#' allows for very wall-clock efficient processing at the cost of 
#' m*n additional double precision numbers allocated.  On the
#' other hand, if \code{inplace=TRUE}, then the wall-clock 
#' performance will drop considerably, but at the memory expense
#' of only m+n additional doubles.  For perspective, given a 
#' 30,000x30,000 matrix, a copy of the data requires an
#' additional 6.7 GiB of data, while the inplace method requires
#' only 469 KiB, a 15,000-fold difference.
#' 
#' Note that cosine is always computed in place.
#' 
#' @section The \code{t} functions:
#' The package also includes "t" functions, like \code{tcosine()}. These
#' behave analogously to \code{tcrossprod()} as \code{crossprod()} in base R.
#' So of \code{cosine()} operates on the columns of the input matrix, then
#' \code{tcosine()} operates on the rows.  Another way to think of it is,
#' \code{tcosine(x) = cosine(t(x))}.
#' 
#' @section Implementation Details:
#' Multiple storage schemes for the input data are accepted.  
#' For dense matrices, an ordinary R matrix input is accepted.  
#' For sparse matrices, a matrix in COO format, namely 
#' \code{simple_triplet_matrix} from the slam package, is accepted.
#' 
#' The implementation for dense matrix inputs is dominated
#' by a symmetric rank-k update via the BLAS subroutine \code{dsyrk};
#' see the package vignette for a discussion of the algorithm
#' implementation and complexity.
#' 
#' The implementation for two dense vector inputs is dominated by the
#' product \code{t(x) \%*\% y} performed by the BLAS subroutine 
#' \code{dgemm} and the normalizing products \code{t(y) \%*\% y},
#' each computed via the BLAS function \code{dsyrk}.
#' 
#' @useDynLib coop, R_co_mat, R_co_mat_pairwise, R_co_vecvec,
#'   R_co_sparse, R_sparsity_int, R_sparsity_dbl,
#'   R_csc_to_coo, R_fast_naomit, R_naomit_vecvec,
#'   R_scaler, R_check_badvals
#' 
#' @docType package
#' @name coop-package
#' @author Drew Schmidt
#' @keywords package
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