updated copyright

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

M DESCRIPTION => DESCRIPTION +1 -1

@@ 2,7 2,7 @@ Package: tdigestType: 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")),

M R/create.R => R/create.R +14 -2

@@ 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

D R/tdigest-package.R => R/tdigest-package.R +0 -25

@@ 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

M inst/COPYRIGHTS => inst/COPYRIGHTS +1 -1

@@ 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:

D man/print.tdigest.Rd => man/print.tdigest.Rd +0 -17

@@ 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}

M man/tdigest.Rd => man/tdigest.Rd +18 -1

@@ 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}

D man/tick-tdigest-package-tick.Rd => man/tick-tdigest-package-tick.Rd +0 -31

@@ 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}