Merge pull request #207 from hneth/master

Minor

DESCRIPTION

@@ -2,7 +2,7 @@ Package: FFTrees
 Type: Package
 Title: Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees
 Version: 2.0.0.9010
-Date: 2024-02-18
+Date: 2024-02-20
 Authors@R: c(person("Nathaniel", "Phillips", role = c("aut"), email = "Nathaniel.D.Phillips.is@gmail.com", comment = c(ORCID = "0000-0002-8969-7013")),
              person("Hansjoerg", "Neth", role = c("aut", "cre"), email = "h.neth@uni.kn", comment = c(ORCID = "0000-0001-5427-3141")),
              person("Jan", "Woike", role = "aut", comment = c(ORCID = "0000-0002-6816-121X")),

README.Rmd

@@ -27,21 +27,20 @@ url_JDM_html  <- "https://journal.sjdm.org/17/17217/jdm17217.html"
 url_JDM_pdf   <- "https://journal.sjdm.org/17/17217/jdm17217.pdf"
 
 url_JDM_doi <- "https://doi.org/10.1017/S1930297500006239"
+doi_JDM     <- "10.1017/S1930297500006239"
 ```
 
 <!-- Title, version and logo: -->
 
 # FFTrees `r packageVersion("FFTrees")` <img src = "./inst/logo.png" align = "right" alt = "FFTrees" width = "160" />
 
-
 <!-- Devel badges start: --> 
 [![CRAN status](https://www.r-pkg.org/badges/version/FFTrees)](https://CRAN.R-project.org/package=FFTrees)
 [![Downloads/month](https://cranlogs.r-pkg.org/badges/FFTrees?color='00a9e0')](https://www.r-pkg.org/pkg/FFTrees)
 [![Total downloads](https://cranlogs.r-pkg.org/badges/grand-total/FFTrees?color='00a9e0')](https://www.r-pkg.org/pkg/FFTrees)
 [![R-CMD-check](https://github.com/ndphillips/FFTrees/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ndphillips/FFTrees/actions/workflows/R-CMD-check.yaml)
 <!-- Devel badges end. -->
 
-
 <!-- Release badges start: -->
 <!-- [![CRAN status](https://www.r-pkg.org/badges/version/FFTrees)](https://CRAN.R-project.org/package=FFTrees) -->
 <!-- [![Total downloads](https://cranlogs.r-pkg.org/badges/grand-total/FFTrees?color='00a9e0')](https://www.r-pkg.org/pkg/FFTrees) -->
@@ -58,7 +57,7 @@ url_JDM_doi <- "https://doi.org/10.1017/S1930297500006239"
 
 <!-- Pkg goal: -->
 
-The R package **FFTrees** creates, visualizes and evaluates _fast-and-frugal decision trees_ (FFTs) for solving binary classification tasks, using the algorithms and methods described in Phillips, Neth, Woike & Gaissmaier (2017, doi\ [10.1017/S1930297500006239](`r url_JDM_doi`) | [html](`r url_JDM_html`) | [PDF](`r url_JDM_pdf`)). 
+The R package **FFTrees** creates, visualizes and evaluates _fast-and-frugal decision trees_ (FFTs) for solving binary classification tasks, using the algorithms and methods described in Phillips, Neth, Woike & Gaissmaier (2017, [`r doi_JDM`](`r url_JDM_doi`)). 
 
 
 ## What are fast-and-frugal trees (FFTs)?
@@ -263,7 +262,7 @@ As a comprehensive, yet accessible introduction to FFTs, we recommend our articl
 - Phillips, N. D., Neth, H., Woike, J. K. & Gaissmaier, W. (2017). 
 FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. 
 _Judgment and Decision Making_, _12_ (4), 344–368. 
-doi\ [10.1017/S1930297500006239](`r url_JDM_doi`) 
+doi\ [`r doi_JDM`](`r url_JDM_doi`) 
 
 
 We encourage you to read the article to learn more about the history of FFTs and how the **FFTrees** package creates, visualizes, and evaluates them. 
@@ -272,7 +271,7 @@ When using **FFTrees** in your own work, please cite us and share your experienc
 
 <!-- Examples uses/publications (with links): -->
 
-By\ 2023, over 100\ scientific publications have used or cited **FFTrees** (see [Google Scholar](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=205528310591558601) for the full list).  
+By\ 2024, over 120\ scientific publications have used or cited **FFTrees** (see [Google Scholar](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=205528310591558601) for the full list). 
 Examples include: 
 
 - Lötsch, J., Haehner, A., & Hummel, T. (2020). Machine-learning-derived rules set excludes risk of Parkinson’s disease in patients with olfactory or gustatory symptoms with high accuracy. 

README.md

@@ -31,9 +31,7 @@ The R package **FFTrees** creates, visualizes and evaluates
 *fast-and-frugal decision trees* (FFTs) for solving binary
 classification tasks, using the algorithms and methods described in
 Phillips, Neth, Woike & Gaissmaier (2017,
-doi [10.1017/S1930297500006239](https://doi.org/10.1017/S1930297500006239)
-\| [html](https://journal.sjdm.org/17/17217/jdm17217.html) \|
-[PDF](https://journal.sjdm.org/17/17217/jdm17217.pdf)).
+[10.1017/S1930297500006239](https://doi.org/10.1017/S1930297500006239)).
 
 ## What are fast-and-frugal trees (FFTs)?
 
@@ -302,11 +300,10 @@ continue developing the package.
 
 <!-- Examples uses/publications (with links): -->
 
-By 2023, over 100 scientific publications have used or cited **FFTrees**
+By 2024, over 120 scientific publications have used or cited **FFTrees**
 (see [Google
 Scholar](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=205528310591558601)
-for the full list).  
-Examples include:
+for the full list). Examples include:
 
 - Lötsch, J., Haehner, A., & Hummel, T. (2020). Machine-learning-derived
   rules set excludes risk of Parkinson’s disease in patients with
@@ -343,6 +340,6 @@ Examples include:
 
 ------------------------------------------------------------------------
 
-\[File `README.Rmd` last updated on 2024-02-19.\]
+\[File `README.Rmd` last updated on 2024-02-20.\]
 
 <!-- eof. -->