📖 JOSS Review: Changed wording.

JonasMoss · Dec 3, 2019 · 0a0c699 · 0a0c699
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -6,7 +6,8 @@ Authors@R: c(
     person("Jonas", "Moss", , "jonas.gjertsen@gmail.com", role = c("aut", "cre"), 
       comment = c(ORCID = "0000-0002-6876-6964")
     ))
-Description: Make univariate density estimation easier, quicker, and more reliable. 
+Description: User-friendly maximum likelihood estimation of univariate 
+    densities.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true

diff --git a/README.Rmd b/README.Rmd
@@ -27,7 +27,8 @@ set.seed(313)
 ```
 
 ## Overview
-[`univariateML`](https://univariateml.netlify.com/index.html) is an `R`-package for fast, easy, and reliable maximum likelihood estimation for a 
+[`univariateML`](https://univariateml.netlify.com/index.html) is an `R`-package for
+user-friendly maximum likelihood estimation of a
 [selection](https://univariateml.netlify.com/articles/distributions.html) of parametric univariate densities. In addition to basic estimation capabilities,
 this package support visualization through `plot` and `qqmlplot`, model selection
 by `AIC` and `BIC`, confidence sets through the parametric bootstrap with

diff --git a/README.md b/README.md
@@ -16,8 +16,7 @@ developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.re
 ## Overview
 
 [`univariateML`](https://univariateml.netlify.com/index.html) is an
-`R`-package for fast, easy, and reliable maximum likelihood estimation
-for a
+`R`-package for user-friendly maximum likelihood estimation of a
 [selection](https://univariateml.netlify.com/articles/distributions.html)
 of parametric univariate densities. In addition to basic estimation
 capabilities, this package support visualization through `plot` and

diff --git a/man/figures/README-weibull_plot-1.png b/man/figures/README-weibull_plot-1.png