Changes to confirm with CRAN requirements.

.Rbuildignore

@@ -28,5 +28,6 @@ cran-comments.md
 ^doc$
 ^Meta$
 gui_aife_studio.Rmd
+classification_tasks.Rmd
 intel_power_gadget_log.csv
 reference

.Rhistory

@@ -1,12 +1,3 @@
-#Callback-------------------------------------------------------------------
-if use_callback==True:
-if bacc_val>best_bacc:
-if trace>=1:
-print("Val Balanced Accuracy increased from {:.4f} to {:.4f}".format(best_bacc,bacc_val))
-print("Save checkpoint to {}".format(filepath))
-#torch.save(model.state_dict(),filepath)
-safetensors.torch.save_model(model=model,filename=filepath)
-best_bacc=bacc_val
 #Finalize--------------------------------------------------------------------
 if use_callback==True:
 if trace>=1:
@@ -510,3 +501,12 @@ install.packages(c("BH", "brew", "brio", "cli", "coda", "commonmark", "curl", "d
 install.packages(c("BH", "brew", "brio", "cli", "coda", "commonmark", "curl", "data.table", "DBI", "desc", "digest", "DT", "e1071", "fansi", "float", "FNN", "glue", "htmlwidgets", "httpuv", "igraph", "ISOcodes", "later", "LiblineaR", "listenv", "lpSolve", "markdown", "Matrix", "MatrixExtra", "mgcv", "MplusAutomation", "pak", "parallelly", "pkgbuild", "pkgload", "processx", "progress", "ps", "psych", "ragg", "Rcpp", "RcppArmadillo", "RCurl", "readODS", "readr", "reticulate", "rJava", "roxygen2", "s2", "sass", "sf", "shinyWidgets", "stringi", "survival", "tensorflow", "tfruns", "tidyr", "topicmodels", "vroom", "withr", "xfun", "yaml", "zip"))
 install.packages("brew")
 install.packages(c("BH",  "brio", "cli", "coda", "commonmark", "curl", "data.table", "DBI", "desc", "digest", "DT", "e1071", "fansi", "float", "FNN", "glue", "htmlwidgets", "httpuv", "igraph", "ISOcodes", "later", "LiblineaR", "listenv", "lpSolve", "markdown", "Matrix", "MatrixExtra", "mgcv", "MplusAutomation", "pak", "parallelly", "pkgbuild", "pkgload", "processx", "progress", "ps", "psych", "ragg", "Rcpp", "RcppArmadillo", "RCurl", "readODS", "readr", "reticulate", "rJava", "roxygen2", "s2", "sass", "sf", "shinyWidgets", "stringi", "survival", "tensorflow", "tfruns", "tidyr", "topicmodels", "vroom", "withr", "xfun", "yaml", "zip"))
+devtools::document()
+devtools::build_site()
+devtools::build_site()
+devtools::build_site()
+devtools::check_win_release()
+devtools::check_win_devel()
+devtools::check_win_devel()
+devtools::check_win_devel()
+devtools::check_win_devel()

README.Rmd

@@ -121,7 +121,7 @@ interface.
 
 *Aifeducation Studio* allows users to easily develop, train,
 apply, document, and analyse AI models without any coding skills. See
-the corresponding vignette for more details: [02a Classification Tasks](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
+the corresponding vignette for more details: [02a Using Aifeducation Studio](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
 
 ## Sustainability
 
@@ -295,9 +295,8 @@ you can find a detailed guide on how to document and share your models.
 
 -   Installation and configuration of the package: [01 Get
     Started](https://fberding.github.io/aifeducation/articles/aifeducation.html).
--   Introduction graphical user interface *Aifeducation Studio*: [02a
-    Classification
-    Tasks](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
+-   Introduction graphical user interface *Aifeducation Studio*: 
+    [02a Using Aifeducation Studio](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
 -   A short introduction into the package with examples for
     classification tasks: [02b Classification
     Tasks](https://fberding.github.io/aifeducation/articles/classification_tasks.html).

README.md

@@ -111,8 +111,8 @@ Studio</figcaption>
 
 *Aifeducation Studio* allows users to easily develop, train, apply,
 document, and analyse AI models without any coding skills. See the
-corresponding vignette for more details: [02a Classification
-Tasks](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
+corresponding vignette for more details: [02a Using Aifeducation
+Studio](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
 
 ## Sustainability
 
@@ -285,8 +285,8 @@ you can find a detailed guide on how to document and share your models.
 - Installation and configuration of the package: [01 Get
   Started](https://fberding.github.io/aifeducation/articles/aifeducation.html).
 - Introduction graphical user interface *Aifeducation Studio*: [02a
-  Classification
-  Tasks](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
+  Using Aifeducation
+  Studio](https://fberding.github.io/aifeducation/articles/gui_aife_studio.html).
 - A short introduction into the package with examples for classification
   tasks: [02b Classification
   Tasks](https://fberding.github.io/aifeducation/articles/classification_tasks.html).

docs/pkgdown.yml

@@ -6,5 +6,5 @@ articles:
   classification_tasks: classification_tasks.html
   gui_aife_studio: gui_aife_studio.html
   sharing_and_publishing: sharing_and_publishing.html
-last_built: 2024-02-16T08:08Z
+last_built: 2024-02-17T19:56Z
 

vignettes/aifeducation.Rmd

@@ -317,7 +317,7 @@ A guide how to use the graphical user interface can be found in vignette
 
 A short introduction into the
 package with examples for classification tasks can be found in vignette
-[02b classification tasks](classification_tasks.html).
+[02b classification tasks](https://fberding.github.io/aifeducation/articles/classification_tasks.html).
 
 Documenting and sharing your work is described in vignette
 [03 sharing and using trained AI/models](sharing_and_publishing.html)

vignettes/classification_tasks.Rmd

@@ -76,7 +76,7 @@ a research area, concepts are not unified. Figure 1 illustrates this
 package's understanding.
 
 ![Figure 1: Understanding of Central
-Concepts](classif_fig_01.png){width="100%"}
+Concepts](img_articles/classif_fig_01.png){width="100%"}
 
 Since *aifeducation* looks at the application of AI for classification
 tasks from the perspective of the empirical method of content analysis,
@@ -98,7 +98,7 @@ to train an AI to classify text in line with supervised machine learning
 principles.
 
 ![Figure 2: Basic Structure of Supervised Machine
-Learning](classif_fig_02.png){width="100%"}
+Learning](img_articles/classif_fig_02.png){width="100%"}
 
 By providing AI with both the textual data as input data and the
 corresponding information about the class as target data, AI can learn
@@ -114,7 +114,7 @@ necessary. Figure 3 provides an overview integrating the functions and
 objects of *aifeducation*.
 
 ![Figure 3: Overview of the Steps to Perform a
-Classification](classif_fig_03.png){width="100%"}
+Classification](img_articles/classif_fig_03.png){width="100%"}
 
 The first step is to transform raw texts into a form computers can use.
 That is, the raw texts must be transformed into numbers. In modern
@@ -988,7 +988,7 @@ With the other parameters you decide about the structure of your
 classifier. Figure 4 illustrates this.
 
 ![Figure 4: Overview of Possible Structure of a
-Classifier](classif_fig_04.png){width="100%"}
+Classifier](img_articles/classif_fig_04.png){width="100%"}
 
 `hidden` takes a vector of integers, determining the number of layers
 and the number of neurons. In our example, there are no dense layers.
@@ -1186,7 +1186,7 @@ Figure 5 illustrates the training loop for the cases that all three
 options are set to `TRUE`.
 
 ![Figure 5: Overview of the Steps to Perform a
-Classification](classif_fig_05.png){width="100%"}
+Classification](img_articles/classif_fig_05.png){width="100%"}
 
 The example above applies the algorithm proposed by Cascante-Bonilla et
 al. (2020). After training the classifier on the labeled data, the
@@ -1281,7 +1281,7 @@ library(iotarelr)
 iotarelr::plot_iota2_alluvial(test_classifier$reliability$iota_object_end_free)
 ```
 
-![Figure 6: Coding Stream of the Classifier](classif_fig_06.png){width="100%"}
+![Figure 6: Coding Stream of the Classifier](img_articles/classif_fig_06.png){width="100%"}
 
 Here you can see that a small number of negative reviews is treated
 as a good review while a larger number of positive reviews is

vignettes/gui_aife_studio.Rmd

@@ -92,7 +92,7 @@ disciplines using different, sometimes overlapping concepts. Even within
 a single research area, concepts are not unified. Figure 1 illustrates
 this package's understanding.
 
-![Figure 1: Understanding of Central Concepts](classif_fig_01.png){width="100%"}
+![Figure 1: Understanding of Central Concepts](img_articles/classif_fig_01.png){width="100%"}
 
 Since *aifeducation* looks at the application of AI for classification
 tasks from the perspective of the empirical method of content analysis,
@@ -113,7 +113,7 @@ labeled with the correct class. As Figure 2 illustrates, two kinds of
 data are necessary to train an AI to classify text in line with
 supervised machine learning principles.
 
-![Figure 2: Basic Structure of Supervised Machine Learning](classif_fig_02.png){width="100%"}
+![Figure 2: Basic Structure of Supervised Machine Learning](img_articles/classif_fig_02.png){width="100%"}
 
 By providing AI with both the textual data as input data and the
 corresponding information about the class as target data, AI can learn
@@ -147,7 +147,7 @@ target data for training AI to classify a text.
 In *aifeducation,* these steps are covered with three different types of
 models, as shown in Figure 3.
 
-![Figure 3: Modells Types in aifeducation](classif_model_hierachy.png){width="100%"}
+![Figure 3: Modells Types in aifeducation](img_articles/classif_model_hierachy.png){width="100%"}
 
 -   **Base Models:** The base models are the models which contain the
     capacities to understand natural language. In general, these are