Update citation information

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: grafzahl
 Title: Supervised Machine Learning for Textual Data Using Transformers and 'Quanteda'
-Version: 0.0.7.9999
+Version: 0.0.8
 Authors@R: 
     person("Chung-hong", "Chan", , "chainsawtiney@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-6232-7530"))
-Description: Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>.
+Description: Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.
 License: GPL (>= 3)
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 URL: https://github.com/chainsawriot/grafzahl
 BugReports: https://github.com/chainsawriot/grafzahl/issues
 Suggests: 

README.Rmd

@@ -25,7 +25,7 @@ If you don't know what I am talking about, don't worry, this package is gracious
 
 Please cite this software as:
 
-Chan, C., (2023). [grafzahl: fine-tuning Transformers for text data from within R](paper/grafzahl_sp.pdf). *Computational Communication Research* (Accepted)
+Chan, C., (2023). [grafzahl: fine-tuning Transformers for text data from within R](paper/grafzahl_sp.pdf). *Computational Communication Research* 5(1): 76-84. [https://doi.org/10.5117/CCR2023.1.003.CHAN](https://doi.org/10.5117/CCR2023.1.003.CHAN)
 
 ## Installation
 

README.md

@@ -26,7 +26,7 @@ Please cite this software as:
 
 Chan, C., (2023). [grafzahl: fine-tuning Transformers for text data from
 within R](paper/grafzahl_sp.pdf). *Computational Communication Research*
-(Accepted)
+5(1): 76-84. <https://doi.org/10.5117/CCR2023.1.003.CHAN>
 
 ## Installation
 

inst/CITATION

@@ -5,6 +5,9 @@ bibentry(bibtype = "article",
          title = "grafzahl: fine-tuning Transformers for text data from within R.",
          journal = "Computational Communication Research",
          author = c(person("Chung-hong", "Chan")),
-         url = "https://github.com/chainsawriot/grafzahl",
+         doi = "10.5117/CCR2023.1.003.CHAN",
+         volume = 5,
+         number = 1,
+         pages = "76-84",
          year = 2023
          )

paper/grafzahl_sp.qmd

@@ -152,7 +152,7 @@ The out-of-sample F1 measures of the fine-tuned model are .76, .67, and .72 (vs
 #| echo: false
 #| fig.cap: Learning curve of machine learning algorithms
 #| label: fig-fig1
-readRDS(here::here("learning.RDS"))
+readRDS(here::here("paper/learning.RDS"))
 ```
 
 [^caret]: The function `confusionMatrix()` can accept the predicted values and ground truth directly, without using `table()` first. But the predicted values and ground truth must be `factor`: `confusionMatrix(as.factor(predicted_sentiment), as.factor(docvars(test_corpus, "value")), mode = "prec_recall")`.