diff --git a/DESCRIPTION b/DESCRIPTION
index 8937532..be46b5d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,27 +1,27 @@
 Package: tdigest
 Type: Package
-Title: Wicked Fast, Accurate Quantiles Using 't-Digests'
+Title: Wicked Fast, Accurate Quantiles Using t-Digests
 Version: 0.3.0
-Date: 2019-07-21
+Date: 2019-07-25
 Authors@R: c(
     person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre"), 
            comment = c(ORCID = "0000-0001-5670-2640")),
     person("Ted", "Dunning", role = "aut",
            comment = "t-Digest algorithm; <https://github.com/tdunning/t-digest/>"),
-    person("ajwerner", "", role = "aut", 
+    person("Andrew", "Werner", role = "aut", 
            comment = "Original C+ code; <https://github.com/ajwerner/tdigest>")
   )
-Maintainer: Bob Rudis <bob@rud.is>
-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 
+Description: The t-Digest construction algorithm, by 
+    Dunning et al., (2019) <arXiv:1902.04023v1>, 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.
+    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. The accuracy of quantile estimates produced 
+    by t-Digests can be orders of magnitude more accurate than those produced 
+    by previous digest algorithms. Methods are provided to create and update 
+    t-Digests and retrieve quantiles from the accumulated distributions.
 URL: https://gitlab.com/hrbrmstr/tdigest
 BugReports: https://gitlab.com/hrbrmstr/tdigest/issues
 Copyright: file inst/COPYRIGHTS
diff --git a/R/create.R b/R/create.R
index ad2772f..63d9692 100644
--- a/R/create.R
+++ b/R/create.R
@@ -1,31 +1,31 @@
-#' Create a new t-digest histogram from a vector
+#' Create a new t-Digest histogram from a vector
 #'
-#' The t-digest construction algorithm uses a variant of 1-dimensional
+#' The t-Digest construction algorithm, by Dunning et al., 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
+#' 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.
+#' 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. The accuracy of quantile estimates produced
+#' by t-Digests can be orders of magnitude more accurate than those produced
+#' by previous digest algorithms. Methods are provided to create and update
+#' t-Digests and retrieve quantiles from the accumulated distributions.
 #'
-#' @param vec vector (will be converted to `double` if not already double). NOTE that this
-#'        is ALTREP-aware and will not materialize the passed-in object in order to
-#'        add the values to the t-Digest.
+#' @param vec vector (will be converted to `double` if not already double).
+#'        NOTE that this is ALTREP-aware and will not materialize the passed-in
+#'        object in order to add the values to the t-Digest.
 #' @param compression the input compression value; should be >= 1.0; this
-#'        will control how aggressively the TDigest compresses data together.
+#'        will control how aggressively the t-Digest compresses data together.
 #'        The original t-Digest paper suggests using a value of 100 for a good
 #'        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.
-#' @export
-#' @return a tdigest object
-#' @references <https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf>
+#' @return a `tdigest` object
+#' @references [Computing Extremely Accurate Quantiles Using t-Digests](https://arxiv.org/abs/1902.04023)
 #' @importFrom stats quantile
 #' @useDynLib tdigest, .registration = TRUE
+#' @export
 #' @examples
 #' set.seed(1492)
 #' x <- sample(0:100, 1000000, replace = TRUE)
@@ -37,13 +37,13 @@ tdigest <- function(vec, compression=100) {
   .Call("Rtdig", vec=vec, compression=compression)
 }
 
-#' Calculate sample quantiles from a t-digest
+#' Calculate sample quantiles from a t-Digest
 #'
-#' @param td t-digest object
+#' @param td t-Digest object
 #' @param probs numeric vector of probabilities with values in range 0:1
 #' @export
-#' @return a numeric vector
-#' @references <https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf>
+#' @return a `numeric` vector containing the requested quantile values
+#' @references <https://raw.githubusercontent.com/tdunning/t-Digest/master/docs/t-Digest-paper/histo.pdf>
 #' @examples
 #' set.seed(1492)
 #' x <- sample(0:100, 1000000, replace = TRUE)
@@ -66,7 +66,7 @@ quantile.tdigest <- function(x, probs = seq(0, 1, 0.25), ...) {
 }
 
 #' @rdname tdigest
-#' @param x t-tigest object
+#' @param x `tdigest` object
 #' @param ... unused
 #' @keywords internal
 #' @export
@@ -87,26 +87,28 @@ print.tdigest <- function(x, ...) {
 #' Allocate a new histogram
 #'
 #' @param compression the input compression value; should be >= 1.0; this
-#'        will control how aggressively the TDigest compresses data together.
+#'        will control how aggressively the t-Digest compresses data together.
 #'        The original t-Digest paper suggests using a value of 100 for a good
 #'        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.
 #' @export
-#' @return a tdigest object
-#' @references <https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf>
+#' @return a `tdigest` object
+#' @references <https://raw.githubusercontent.com/tdunning/t-Digest/master/docs/t-Digest-paper/histo.pdf>
 #' @examples
 #' td <- td_create(10)
 td_create <- function(compression=100) {
+  stopifnot(compression >= 1.0)
   compression <- as.double(compression[1])
   .Call("Rtd_create", compression=compression, PACKAGE="tdigest")
 }
 
-#' Total items contained in the t-digest
+#' Total items contained in the t-Digest
 #'
-#' @param td t-digest object
+#' @param td t-Digest object
 #' @export
+#' @return `double` containing the size of the t-Digest
 #' @examples
 #' td <- td_create(10)
 #' td_add(td, 0, 1)
@@ -118,11 +120,12 @@ td_total_count <- function(td) {
   .Call("Rtd_total_count", td=td, PACKAGE="tdigest")
 }
 
-#' Add a value to the t-digest with the specified count
+#' Add a value to the t-Digest with the specified count
 #'
-#' @param td t-digest object
+#' @param td t-Digest object
 #' @param val value
 #' @param count count
+#' @return the original, updated `tdigest` object
 #' @export
 #' @examples
 #' td <- td_create(10)
@@ -138,9 +141,10 @@ td_add <- function(td, val, count) {
 
 #' Return the value at the specified quantile
 #'
-#' @param td t-digest object
+#' @param td t-Digest object
 #' @param q quantile (range 0:1)
 #' @export
+#' @return the computed quantile (`double`)
 #' @examples
 #' td <- td_create(10)
 #'
@@ -160,8 +164,9 @@ td_value_at <- function(td, q) {
 
 #' Return the quantile of the value
 #'
-#' @param td t-digest object
+#' @param td t-Digest object
 #' @param val value
+#' @return the computed quantile (`double`)
 #' @export
 td_quantile_of <- function(td, val) {
   stopifnot(inherits(td, "tdigest"))
@@ -170,11 +175,11 @@ td_quantile_of <- function(td, val) {
   .Call("Rtd_quantile_of", tdig=td, val=val, PACKAGE="tdigest")
 }
 
-#' Merge one t-digest into another
+#' Merge one t-Digest into another
 #'
-#' @param from,into t-digests
+#' @param from,into t-Digests
 #' @return `into`
-#' @return a tdigest object
+#' @return a `tdigest` object
 #' @export
 td_merge <- function(from, into) {
   stopifnot(inherits(from, "tdigest"))
diff --git a/README.Rmd b/README.Rmd
index 380d167..b4a9095 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -19,9 +19,18 @@ 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.
-
-See [the original paper by Ted Dunning](https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf) for more details on 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. The accuracy of quantile estimates produced 
+by t-Digests can be orders of magnitude more accurate than those produced 
+by previous digest algorithms. Methods are provided to create and update 
+t-Digests and retreive quantiles from the accumulated distributions.
+
+See [the original paper by Ted Dunning & Otmar Ertl](https://arxiv.org/abs/1902.04023) for more details on t-Digests.
 
 ## What's Inside The Tin
 
diff --git a/inst/COPYRIGHTS b/inst/COPYRIGHTS
index a5e5b0f..5e1a721 100644
--- a/inst/COPYRIGHTS
+++ b/inst/COPYRIGHTS
@@ -1,8 +1,15 @@
-The R code and src/tdigest-main.c, src/init.c are MIT-licensed by the package author.
+The R code and src/tdigest-main.c, src/init.c are MIT-licensed
+by the package author.
 
-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]
+src/tdigest.h, src/tdigest.c are MIT-licensed & Copyright (c) 2018
+Andrew Werner [REF: https://github.com/ajwerner/tdigestc; license below
+copied from that repository]
 
-The original t-Digest implementation and algorithm are  have the following license:
+The t-Digest original algorithm and Java implementation
+(which is the base of Andrew Werner's re-implementation) is
+Copyright (c) 2015 Ted Dunning and uses the same Apache License2.0, below.
+
+------------
 
                                 Apache License
                            Version 2.0, January 2004
diff --git a/man/td_add.Rd b/man/td_add.Rd
index 12426a2..4d04d58 100644
--- a/man/td_add.Rd
+++ b/man/td_add.Rd
@@ -2,19 +2,22 @@
 % Please edit documentation in R/create.R
 \name{td_add}
 \alias{td_add}
-\title{Add a value to the t-digest with the specified count}
+\title{Add a value to the t-Digest with the specified count}
 \usage{
 td_add(td, val, count)
 }
 \arguments{
-\item{td}{t-digest object}
+\item{td}{t-Digest object}
 
 \item{val}{value}
 
 \item{count}{count}
 }
+\value{
+the original, updated \code{tdigest} object
+}
 \description{
-Add a value to the t-digest with the specified count
+Add a value to the t-Digest with the specified count
 }
 \examples{
 td <- td_create(10)
diff --git a/man/td_create.Rd b/man/td_create.Rd
index caf32b4..1efa60a 100644
--- a/man/td_create.Rd
+++ b/man/td_create.Rd
@@ -11,7 +11,7 @@ is_tdigest(td)
 }
 \arguments{
 \item{compression}{the input compression value; should be >= 1.0; this
-will control how aggressively the TDigest compresses data together.
+will control how aggressively the t-Digest compresses data together.
 The original t-Digest paper suggests using a value of 100 for a good
 balance between precision and efficiency. It will land at very small
 (think like 1e-6 percentile points) errors at extreme points in the
@@ -21,7 +21,7 @@ distribution, and compression ratios of around 500 for large data sets
 \item{td}{t-digest object}
 }
 \value{
-a tdigest object
+a \code{tdigest} object
 }
 \description{
 Allocate a new histogram
@@ -30,5 +30,5 @@ Allocate a new histogram
 td <- td_create(10)
 }
 \references{
-\url{https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf}
+\url{https://raw.githubusercontent.com/tdunning/t-Digest/master/docs/t-Digest-paper/histo.pdf}
 }
diff --git a/man/td_merge.Rd b/man/td_merge.Rd
index e512d43..e1bfaec 100644
--- a/man/td_merge.Rd
+++ b/man/td_merge.Rd
@@ -2,18 +2,18 @@
 % Please edit documentation in R/create.R
 \name{td_merge}
 \alias{td_merge}
-\title{Merge one t-digest into another}
+\title{Merge one t-Digest into another}
 \usage{
 td_merge(from, into)
 }
 \arguments{
-\item{from, into}{t-digests}
+\item{from, into}{t-Digests}
 }
 \value{
 \code{into}
 
-a tdigest object
+a \code{tdigest} object
 }
 \description{
-Merge one t-digest into another
+Merge one t-Digest into another
 }
diff --git a/man/td_quantile_of.Rd b/man/td_quantile_of.Rd
index 09e87de..1aba486 100644
--- a/man/td_quantile_of.Rd
+++ b/man/td_quantile_of.Rd
@@ -7,10 +7,13 @@
 td_quantile_of(td, val)
 }
 \arguments{
-\item{td}{t-digest object}
+\item{td}{t-Digest object}
 
 \item{val}{value}
 }
+\value{
+the computed quantile (\code{double})
+}
 \description{
 Return the quantile of the value
 }
diff --git a/man/td_total_count.Rd b/man/td_total_count.Rd
index 971e7c3..689d8c4 100644
--- a/man/td_total_count.Rd
+++ b/man/td_total_count.Rd
@@ -3,19 +3,22 @@
 \name{td_total_count}
 \alias{td_total_count}
 \alias{length.tdigest}
-\title{Total items contained in the t-digest}
+\title{Total items contained in the t-Digest}
 \usage{
 td_total_count(td)
 
 \method{length}{tdigest}(x)
 }
 \arguments{
-\item{td}{t-digest object}
+\item{td}{t-Digest object}
 
 \item{x}{a tdigest object}
 }
+\value{
+\code{double} containing the size of the t-Digest
+}
 \description{
-Total items contained in the t-digest
+Total items contained in the t-Digest
 }
 \examples{
 td <- td_create(10)
diff --git a/man/td_value_at.Rd b/man/td_value_at.Rd
index 3ab239d..ae34cb3 100644
--- a/man/td_value_at.Rd
+++ b/man/td_value_at.Rd
@@ -10,7 +10,7 @@ td_value_at(td, q)
 \method{[}{tdigest}(x, i, ...)
 }
 \arguments{
-\item{td}{t-digest object}
+\item{td}{t-Digest object}
 
 \item{q}{quantile (range 0:1)}
 
@@ -20,6 +20,9 @@ td_value_at(td, q)
 
 \item{...}{unused}
 }
+\value{
+the computed quantile (\code{double})
+}
 \description{
 Return the value at the specified quantile
 }
diff --git a/man/tdigest.Rd b/man/tdigest.Rd
index e2a073d..7a8b285 100644
--- a/man/tdigest.Rd
+++ b/man/tdigest.Rd
@@ -3,43 +3,43 @@
 \name{tdigest}
 \alias{tdigest}
 \alias{print.tdigest}
-\title{Create a new t-digest histogram from a vector}
+\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). NOTE that this
-is ALTREP-aware and will not materialize the passed-in object in order to
-add the values to the t-Digest.}
+\item{vec}{vector (will be converted to \code{double} if not already double).
+NOTE that this is ALTREP-aware and will not materialize the passed-in
+object in order to add the values to the t-Digest.}
 
 \item{compression}{the input compression value; should be >= 1.0; this
-will control how aggressively the TDigest compresses data together.
+will control how aggressively the t-Digest compresses data together.
 The original t-Digest paper suggests using a value of 100 for a good
 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{x}{\code{tdigest} object}
 
 \item{...}{unused}
 }
 \value{
-a tdigest object
+a \code{tdigest} object
 }
 \description{
-The t-digest construction algorithm uses a variant of 1-dimensional
+The t-Digest construction algorithm, by Dunning et al., 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
+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.
+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. The accuracy of quantile estimates produced
+by t-Digests can be orders of magnitude more accurate than those produced
+by previous digest algorithms. Methods are provided to create and update
+t-Digests and retrieve quantiles from the accumulated distributions.
 }
 \examples{
 set.seed(1492)
@@ -49,6 +49,6 @@ tquantile(td, c(0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))
 quantile(td)
 }
 \references{
-\url{https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf}
+\href{https://arxiv.org/abs/1902.04023}{Computing Extremely Accurate Quantiles Using t-Digests}
 }
 \keyword{internal}
diff --git a/man/tquantile.Rd b/man/tquantile.Rd
index e5e2cc1..5f9ee4a 100644
--- a/man/tquantile.Rd
+++ b/man/tquantile.Rd
@@ -3,14 +3,14 @@
 \name{tquantile}
 \alias{tquantile}
 \alias{quantile.tdigest}
-\title{Calculate sample quantiles from a t-digest}
+\title{Calculate sample quantiles from a t-Digest}
 \usage{
 tquantile(td, probs)
 
 \method{quantile}{tdigest}(x, probs = seq(0, 1, 0.25), ...)
 }
 \arguments{
-\item{td}{t-digest object}
+\item{td}{t-Digest object}
 
 \item{probs}{numeric vector of probabilities with values in range 0:1}
 
@@ -19,10 +19,10 @@ tquantile(td, probs)
 \item{...}{unused}
 }
 \value{
-a numeric vector
+a \code{numeric} vector containing the requested quantile values
 }
 \description{
-Calculate sample quantiles from a t-digest
+Calculate sample quantiles from a t-Digest
 }
 \examples{
 set.seed(1492)
@@ -32,5 +32,5 @@ tquantile(td, c(0, .01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))
 quantile(td)
 }
 \references{
-\url{https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf}
+\url{https://raw.githubusercontent.com/tdunning/t-Digest/master/docs/t-Digest-paper/histo.pdf}
 }
diff --git a/src/tdigest.h b/src/tdigest.h
index 6310ec3..8357ee3 100644
--- a/src/tdigest.h
+++ b/src/tdigest.h
@@ -6,17 +6,15 @@
 // Copyright (c) 2018 Andrew Werner, All rights reserved.
 //
 // tdigest is an implementation of Ted Dunning's streaming quantile estimation
-// data structure. 
+// data structure.
 // This implementation is intended to be like the new MergingHistogram.
 // It focuses on being in portable C that should be easy to integrate into other
-// languages. In particular it provides mechanisms to preallocate all memory 
+// languages. In particular it provides mechanisms to preallocate all memory
 // at construction time.
 //
-// The implementation is a direct descendent of 
+// The implementation is a direct descendent of
 //  https://github.com/tdunning/t-digest/
 //
-// TODO: add a Ted Dunning Copyright notice.
-//
 ////////////////////////////////////////////////////////////////////////////////
 
 #include <stdlib.h>