7 files changed, 34 insertions(+), 78 deletions(-)

M DESCRIPTION
M R/create.R
D R/tdigest-package.R
M inst/COPYRIGHTS
D man/print.tdigest.Rd
M man/tdigest.Rd
D man/tick-tdigest-package-tick.Rd

@@ 2,7 2,7 @@ Package: tdigest
 Type: Package
 Title: Wicked Fast, Accurate Quantiles Using 't-Digests'
 Version: 0.2.0
-Date: 2019-04-03
+Date: 2019-07-21
 Authors@R: c(
     person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
            comment = c(ORCID = "0000-0001-5670-2640")),

@@ 1,5 1,16 @@
 #' Create a new t-digest histogram from a vector
 #'
+#' The t-digest construction algorithm uses a variant of 1-dimensional
+#' k-means clustering to produce a very compact data structure that allows
+#' accurate estimation of quantiles. This t-digest data structure can be used
+#' to estimate quantiles, compute other rank statistics or even to estimate
+#' related measures like trimmed means. The advantage of the t-digest over
+#' previous digests for this purpose is that the t-digest handles data with
+#' full floating point resolution. With small changes, the t-digest can handle
+#' values from any ordered set for which we can compute something akin to a mean.
+#' The accuracy of quantile estimates produced by t-digests can be orders of
+#' magnitude more accurate than those produced by previous digest algorithms.
+#'
 #' @param vec vector (will be converted to `double` if not already double)
 #' @param compression the input compression value; should be >= 1.0; this
 #'        will control how aggressively the TDigest compresses data together.


@@ 11,6 22,8 @@
 #' @export
 #' @return a tdigest object
 #' @references <https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf>
+#' @importFrom stats quantile
+#' @useDynLib tdigest, .registration = TRUE
 #' @examples
 #' set.seed(1492)
 #' x <- sample(0:100, 1000000, replace = TRUE)


@@ 50,8 63,7 @@ quantile.tdigest <- function(x, probs = seq(0, 1, 0.25), ...) {
   tquantile(x, probs=probs)
 }
 
-#' Printer for t-idgest objects
-#'
+#' @rdname tdigest
 #' @param x t-tigest object
 #' @param ... unused
 #' @keywords internal

@@ 1,25 0,0 @@
-#' Wicked Fast, Accurate Quantiles Using 't-Digests'
-#'
-#' The t-digest construction algorithm uses a variant of 1-dimensional
-#' k-means clustering to produce a very compact data structure that allows
-#' accurate estimation of quantiles. This t-digest data structure can be used
-#' to estimate quantiles, compute other rank statistics or even to estimate
-#' related measures like trimmed means. The advantage of the t-digest over
-#' previous digests for this purpose is that the t-digest handles data with
-#' full floating point resolution. With small changes, the t-digest can handle
-#' values from any ordered set for which we can compute something akin to a mean.
-#' The accuracy of quantile estimates produced by t-digests can be orders of
-#' magnitude more accurate than those produced by previous digest algorithms.
-#'
-#' - URL: <https://gitlab.com/hrbrmstr/tdigest>
-#' - BugReports: <https://gitlab.com/hrbrmstr/tdigest/issues>
-#'
-#' @md
-#' @name `tdigest-package`
-#' @keywords internal
-#' @docType package
-#' @author Bob Rudis (bob@@rud.is)
-#' @importFrom stats quantile
-#' @references <https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf>
-#' @useDynLib tdigest, .registration = TRUE
-NULL>
\ No newline at end of file

@@ 1,6 1,6 @@
 The R code and src/tdigest-main.c, src/init.c are MIT-licensed by the package author.
 
-src/tdigest.h, src/tdigest.c is MIT-licensed & Copyright (c) 2018 ajwerner [REF: https://github.com/ajwerner/tdigestc]
+src/tdigest.h, src/tdigest.c are MIT-licensed & Copyright (c) 2018 ajwerner [REF: https://github.com/ajwerner/tdigestc; license below copied from that repository]
 
 The original t-Digest implementation and algorithm are  have the following license:
 

@@ 1,17 0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/create.R
-\name{print.tdigest}
-\alias{print.tdigest}
-\title{Printer for t-idgest objects}
-\usage{
-\method{print}{tdigest}(x, ...)
-}
-\arguments{
-\item{x}{t-tigest object}
-
-\item{...}{unused}
-}
-\description{
-Printer for t-idgest objects
-}
-\keyword{internal}

@@ 2,9 2,12 @@
 % Please edit documentation in R/create.R
 \name{tdigest}
 \alias{tdigest}
+\alias{print.tdigest}
 \title{Create a new t-digest histogram from a vector}
 \usage{
 tdigest(vec, compression = 100)
+
+\method{print}{tdigest}(x, ...)
 }
 \arguments{
 \item{vec}{vector (will be converted to \code{double} if not already double)}


@@ 16,12 19,25 @@ balance between precision and efficiency. It will land at very small
 (think like 1e-6 percentile points) errors at extreme points in the
 distribution, and compression ratios of around 500 for large data sets
 (~1 million datapoints). Defaults to 100.}
+
+\item{x}{t-tigest object}
+
+\item{...}{unused}
 }
 \value{
 a tdigest object
 }
 \description{
-Create a new t-digest histogram from a vector
+The t-digest construction algorithm uses a variant of 1-dimensional
+k-means clustering to produce a very compact data structure that allows
+accurate estimation of quantiles. This t-digest data structure can be used
+to estimate quantiles, compute other rank statistics or even to estimate
+related measures like trimmed means. The advantage of the t-digest over
+previous digests for this purpose is that the t-digest handles data with
+full floating point resolution. With small changes, the t-digest can handle
+values from any ordered set for which we can compute something akin to a mean.
+The accuracy of quantile estimates produced by t-digests can be orders of
+magnitude more accurate than those produced by previous digest algorithms.
 }
 \examples{
 set.seed(1492)


@@ 33,3 49,4 @@ quantile(td)
 \references{
 \url{https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf}
 }
+\keyword{internal}

@@ 1,31 0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/tdigest-package.R
-\docType{package}
-\name{`tdigest-package`}
-\alias{`tdigest-package`}
-\title{Wicked Fast, Accurate Quantiles Using 't-Digests'}
-\description{
-The t-digest construction algorithm uses a variant of 1-dimensional
-k-means clustering to produce a very compact data structure that allows
-accurate estimation of quantiles. This t-digest data structure can be used
-to estimate quantiles, compute other rank statistics or even to estimate
-related measures like trimmed means. The advantage of the t-digest over
-previous digests for this purpose is that the t-digest handles data with
-full floating point resolution. With small changes, the t-digest can handle
-values from any ordered set for which we can compute something akin to a mean.
-The accuracy of quantile estimates produced by t-digests can be orders of
-magnitude more accurate than those produced by previous digest algorithms.
-}
-\details{
-\itemize{
-\item URL: \url{https://gitlab.com/hrbrmstr/tdigest}
-\item BugReports: \url{https://gitlab.com/hrbrmstr/tdigest/issues}
-}
-}
-\references{
-\url{https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf}
-}
-\author{
-Bob Rudis (bob@rud.is)
-}
-\keyword{internal}