Merge pull request #31 from JonasMoss/cran-release

Cran release
JonasMoss · Dec 18, 2019 · 046b72b · 046b72b
2 parents 0a0c699 + 6969efb
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diff --git a/CRAN-RELEASE b/CRAN-RELEASE
@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2019-12-13.
+Once it is accepted, delete this file and tag the release (commit f9cfbd05c9).
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -6,8 +6,8 @@ Authors@R: c(
     person("Jonas", "Moss", , "jonas.gjertsen@gmail.com", role = c("aut", "cre"), 
       comment = c(ORCID = "0000-0002-6876-6964")
     ))
-Description: User-friendly maximum likelihood estimation of univariate 
-    densities.
+Description: User-friendly maximum likelihood estimation (Fisher (1921) 
+    <doi:10.1098/rsta.1922.0009>) of univariate densities.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true

diff --git a/LICENSE b/LICENSE
@@ -1,21 +1,2 @@
-# MIT License
-
-Copyright (c) 2019 Jonas Moss
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+YEAR: 2019
+COPYRIGHT HOLDER: Jonas Moss
diff --git a/R/bootstrap.R b/R/bootstrap.R
@@ -41,7 +41,7 @@
 #'
 #' @seealso [confint()] for an application of `bootstrapml`.
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' set.seed(1)
 #' object <- mlgamma(mtcars$qsec)
 #'

diff --git a/R/functions.R b/R/functions.R
@@ -32,26 +32,14 @@
 #' obj <- mlnorm(airquality$Wind)
 #' dml(0.5, obj) == dnorm(0.5, mean = obj[1], sd = obj[2])
 #'
-#' ## We study the Beta prime model applied to the airquality data set.
 #' obj <- mlbetapr(airquality$Wind)
 #'
-#' ## Example copied from 'stats::dnorm'.
-#' par(mfrow = c(2, 1))
+#' # Plot the logarithm of the beta prime distribution.
 #' plot(function(x) dml(x, obj, log = TRUE),
 #'   from = 0, to = 20,
 #'   main = "Logarithm of Density", ylab = NA, lwd = 2
 #' )
-#' curve(log(dml(x, obj)), add = TRUE, col = "red", lwd = 2, lty = 2)
-#' mtext("dml(x, obj, log = TRUE)", adj = 0)
-#' mtext("log(dml(x, obj))", col = "red", adj = 1)
 #'
-#' plot(function(x) pml(x, obj, log = TRUE),
-#'   from = 0, to = 20,
-#'   main = "Logarithm of Cumulative Probability", ylab = NA, lwd = 2
-#' )
-#' curve(log(pml(x, obj)), add = TRUE, col = "red", lwd = 2, lty = 2)
-#' mtext("pml(x, obj, log = TRUE)", adj = 0)
-#' mtext("log(pml(x, obj))", col = "red", adj = 1)
 #' @name MaximumLikelihoodDistribution
 #' @export
 dml <- function(x, obj, log = FALSE) {

diff --git a/README.Rmd b/README.Rmd
@@ -19,6 +19,8 @@ knitr::opts_chunk$set(
 [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/JonasMoss/univariateML?branch=master&svg=true)](https://ci.appveyor.com/project/JonasMoss/univariateML)
 [![Coverage Status](https://codecov.io/gh/JonasMoss/univariateML/branch/master/graph/badge.svg)](https://codecov.io/gh/JonasMoss/univariateML?branch=master)
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.01863/status.svg)](https://doi.org/10.21105/joss.01863)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/univariateML)](https://cran.r-project.org/package=univariateML)
 
 ```{r setup, include = FALSE}
 knitr::opts_chunk$set(echo = TRUE)
@@ -37,8 +39,13 @@ function, quantile function, and random sampling at the estimated distribution
 parameters.
 
 ## Installation
-Use the following command from inside `R`:
+Use the following command from inside `R` to install from CRAN.
 ```{r install, echo = TRUE, eval = FALSE}
+install.packages("univariateML")
+```
+
+Or install the development version from Github.
+```{r install_github, echo = TRUE, eval = FALSE}
 # install.packages("devtools")
 devtools::install_github("JonasMoss/univariateML")
 ```

diff --git a/README.md b/README.md
@@ -12,6 +12,8 @@ Status](https://codecov.io/gh/JonasMoss/univariateML/branch/master/graph/badge.s
 [![Project Status: Active – The project has reached a stable, usable
 state and is being actively
 developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.01863/status.svg)](https://doi.org/10.21105/joss.01863)
+[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/univariateML)](https://cran.r-project.org/package=univariateML)
 
 ## Overview
 
@@ -27,7 +29,13 @@ random sampling at the estimated distribution parameters.
 
 ## Installation
 
-Use the following command from inside `R`:
+Use the following command from inside `R` to install from CRAN.
+
+``` r
+install.packages("univariateML")
+```
+
+Or install the development version from Github.
 
 ``` r
 # install.packages("devtools")
@@ -74,9 +82,9 @@ microbenchmark::microbenchmark(
   univariateML = univariateML::mlbeta(x),
   naive = nlm(function(p) -sum(dbeta(x, p[1], p[2], log = TRUE)), p = c(1, 1)))
 #> Unit: microseconds
-#>          expr     min       lq      mean   median       uq     max neval
-#>  univariateML   864.2  1102.90  1305.743  1192.30  1340.95  6007.9   100
-#>         naive 36398.0 39097.35 41108.804 40530.15 43035.50 50693.3   100
+#>          expr       min        lq      mean    median        uq      max neval
+#>  univariateML   925.401  1339.002  2309.141  1708.051  2065.551  41581.2   100
+#>         naive 44287.601 47740.601 54418.306 51779.401 57391.700 104036.7   100
 ```
 
 The maximum likelihood estimators in this package have all been subject

diff --git a/cran-comments.md b/cran-comments.md
@@ -0,0 +1,18 @@
+## Resubmission
+
+* Added reference to Fisher (1921) for maximum likelihood estimation.
+* Removed installation of packages in copula vignette.
+* Replaced \dontrun{} by \donttest{} in Rd-files.
+* Removed examples using par(mfrow = c(2, 1))
+
+## Test environments
+* local Windows 10, R version R version 3.6.1
+* Windows Server 2012 (on AppVeyor) R version 3.6.1 Patched (2019-10-31 r77367)
+* Ubuntu 14.04.5 LTS (on Travis-CI), R version 3.6.1, R-oldrel, R-devel.
+* macOS 10.13.3 (on Travis-CI), R version 3.6.1
+
+## R CMD check results
+There were no ERRORs, WARNINGs or NOTEs. 
+
+## Downstream dependencies
+There are no downstream dependencies.
diff --git a/inst/CITATION b/inst/CITATION
@@ -0,0 +1,16 @@
+citHeader("To cite univariateML in publications, please use:")
+
+citEntry(entry="Article",
+  doi = "10.21105/joss.01863",
+  url = "https://doi.org/10.21105/joss.01863",
+  year = "2019",
+  month = "dec",
+  publisher = "The Open Journal",
+  volume = "4",
+  number = "44",
+  pages = "1863",
+  author = "Jonas Moss",
+  title = "univariateML: An R package for maximum likelihood estimation of univariate densities",
+  journal = "Journal of Open Source Software",
+  textVersion = "Moss, (2019). univariateML: An R package for maximum likelihood estimation of univariate densities. Journal of Open Source Software, 4(44), 1863, https://doi.org/10.21105/joss.01863"
+)
diff --git a/man/MaximumLikelihoodDistribution.Rd b/man/MaximumLikelihoodDistribution.Rd
diff --git a/man/bootstrapml.Rd b/man/bootstrapml.Rd
diff --git a/vignettes/copula.Rmd b/vignettes/copula.Rmd
@@ -8,16 +8,6 @@ vignette: >
 bibliography: copula.bib
 ---
 
-```{R depends, echo = FALSE}
-if (!require("copula")) install.packages("copula", 
-                                         dependencies = TRUE,
-                                         repos = "http://cran.us.r-project.org")
-
-if (!require("dplyr")) install.packages("dplyr", 
-                                        dependencies = TRUE,
-                                        repos = "http://cran.us.r-project.org")
-```
-
 This vignette shows how to model multivariate distributions with 
 [copulas](https://en.wikipedia.org/wiki/Copula_(probability_theory))
 using `univariateML` and the `copula` package. 

diff --git a/vignettes/figure/bootstrap_example_histogram-1.png b/vignettes/figure/bootstrap_example_histogram-1.png
diff --git a/vignettes/overview.Rmd b/vignettes/overview.Rmd
@@ -80,7 +80,7 @@ summary(mlweibull(egypt$age))
 ```
 
 ## Quantile-quantile Plots
-Now we will investigate how the two models differ with [https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot](quantile-quantile plots), or Q-Q plots for short.
+Now we will investigate how the two models differ with [quantile-quantile plots](https://en.wikipedia.org/wiki/Q-Q_plot), or Q-Q plots for short.
 
 ```{r, qqplots}
 qqmlplot(egypt$age, mlweibull, datax = TRUE, main = "QQ Plot for Ancient Egypt")

diff --git a/vignettes/overview.md b/vignettes/overview.md
@@ -134,7 +134,7 @@ summary(mlweibull(egypt$age))
 ```
 
 ## Quantile-quantile Plots
-Now we will investigate how the two models differ with [https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot](quantile-quantile plots), or Q-Q plots for short.
+Now we will investigate how the two models differ with [quantile-quantile plots](https://en.wikipedia.org/wiki/Q-Q_plot), or Q-Q plots for short.
 
 
 ```r
@@ -182,8 +182,8 @@ bootstrapml(mlweibull(egypt$age)) # same as confint(mlweibull(egypt$age))
 
 ```
 ##            2.5%     97.5%
-## shape  1.250344  1.616963
-## scale 29.607607 37.750506
+## shape  1.246644  1.614056
+## scale 29.301693 38.126512
 ```
 
 ```r
@@ -192,8 +192,8 @@ bootstrapml(mlgamma(egypt$age))
 
 ```
 ##             2.5%      97.5%
-## shape 1.34432954 2.03663781
-## rate  0.04113922 0.06793162
+## shape 1.33568486 2.04379587
+## rate  0.04193431 0.06895511
 ```
 
 These confidence intervals are not directly comparable. That is, the `scale` parameter in
@@ -218,7 +218,7 @@ bootstrapml(mlweibull(egypt$age),
 
 ```
 ##       5%      95% 
-## 27.62843 33.73309
+## 27.53889 33.76252
 ```
 
 ```r
@@ -230,7 +230,7 @@ bootstrapml(mlgamma(egypt$age),
 
 ```
 ##       5%      95% 
-## 27.53158 33.91414
+## 27.31093 34.40263
 ```
 
 We are be interested in the quantiles of the underlying distribution,
@@ -246,7 +246,7 @@ bootstrapml(mlweibull(egypt$age),
 
 ```
 ##       5%      95% 
-## 23.03270 28.99678
+## 22.93329 28.84853
 ```
 
 ```r
@@ -257,7 +257,7 @@ bootstrapml(mlgamma(egypt$age),
 
 ```
 ##       5%      95% 
-## 21.83003 27.51890
+## 21.81757 27.57722
 ```
 
 We can also plot the bootstrap samples.
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,2 @@
		This package was submitted to CRAN on 2019-12-13.
		Once it is accepted, delete this file and tag the release (commit f9cfbd05c9).