### Get Example Paper Path from Installed Package

Source: https://github.com/massimoaria/contentanalysis/blob/master/inst/examples/README.md

Retrieves the file path for the example paper using the `get_example_paper()` function. This is the recommended method for users of the installed package. The function will download the file if it's not found locally.

```r
paper_path <- get_example_paper()
```

--------------------------------

### Get Example Paper Path from GitHub

Source: https://github.com/massimoaria/contentanalysis/blob/master/inst/examples/README.md

Retrieves the file path for the example paper directly from the GitHub repository. Use this when developing or if the package is not yet installed.

```r
paper_path <- system.file('examples', 'example_paper.pdf', package = 'contentanalysis')
```

--------------------------------

### GPL Notice for Interactive Program Startup

Source: https://github.com/massimoaria/contentanalysis/blob/master/LICENSE.md

Display this notice when a program starts in interactive mode to inform users about its free software status, warranty, and redistribution conditions.

```text
<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type 'show c' for details.
```

--------------------------------

### Add New Fixture Test

Source: https://github.com/massimoaria/contentanalysis/blob/master/tests/testthat/fixtures/README.md

Example of how to add a test for a new PDF fixture. Ensure pdftools is installed and the fixture file exists.

```R
it("extracts from your-new-fixture.pdf", {
  skip_if_not_installed("pdftools")
  skip_if_no_fixture("your-new-fixture.pdf")
  
  pdf_file <- fixture_path("your-new-fixture.pdf")
  result <- pdf2txt_multicolumn_safe(pdf_file)
  
  expect_type(result, "character")
  expect_gt(nchar(result), 0)
  # Add specific expectations based on known content
})
```

--------------------------------

### Download Example Scientific Paper

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Download a sample scientific paper from a specified URL to a local file. This is useful for testing the analysis functions with a real-world example. Ensure the 'destfile' path is writable.

```r
paper_url <- "https://raw.githubusercontent.com/massimoaria/contentanalysis/master/inst/examples/example_paper.pdf"
download.file(paper_url, destfile = "example_paper.pdf", mode = "wb")
```

--------------------------------

### Install contentanalysis Package from GitHub

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Install the development version of the contentanalysis package directly from GitHub using the devtools package. Ensure devtools is installed first.

```r
# install.packages("devtools")
devtools::install_github("massimoaria/contentanalysis")
```

--------------------------------

### Standard GPL Copyright Notice for Source Files

Source: https://github.com/massimoaria/contentanalysis/blob/master/LICENSE.md

Include this notice at the start of each source file to state the exclusion of warranty and provide copyright information. It points to the full license text.

```text
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Calculate and View Word Distribution by Section

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Tracks the frequency of specified terms across different sections of a document. Use this to understand how specific keywords are distributed throughout the text, for example, to analyze the prevalence of disease-related terms in different segments.

```r
# Track disease-related terms
disease_terms <- c("covid", "pandemic", "health", "policy", "vaccination")
dist <- calculate_word_distribution(doc, disease_terms, use_sections = TRUE)

# View frequencies by section
dist %>%
  select(segment_name, word, count, percentage) %>%
  arrange(segment_name, desc(percentage))
#> # A tibble: 1 × 4
#>   segment_name word   count percentage
#>   <chr>        <chr>  <int>      <dbl>
#> 1 Conclusion   health     1      0.330

# Visualize trends
#plot_word_distribution(dist, plot_type = "area", smooth = FALSE)
```

--------------------------------

### Generate Test Fixtures via Command Line

Source: https://github.com/massimoaria/contentanalysis/blob/master/tests/testthat/fixtures/README.md

Execute this command in the terminal to regenerate the test fixtures using an Rscript.

```bash
Rscript create_test_fixtures.R
```

--------------------------------

### Generate Test Fixtures

Source: https://github.com/massimoaria/contentanalysis/blob/master/tests/testthat/fixtures/README.md

Run this R code to regenerate the test fixtures. Ensure the 'create_test_fixtures.R' script is available.

```r
source("create_test_fixtures.R")
main()
```

--------------------------------

### Set Google Gemini API Key in .Renviron

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Persistently set the GEMINI_API_KEY environment variable for Google Gemini API access by adding it to your .Renviron file.

```r
# Add to ~/.Renviron
GEMINI_API_KEY=your-api-key-here
```

--------------------------------

### Set OpenAlex API Key in .Rprofile

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Automatically set your OpenAlex API key at R startup by adding the configuration to your .Rprofile file.

```r
# Add to ~/.Rprofile
openalexR::oa_apikey("your-api-key-here")
```

--------------------------------

### Set Google Gemini API Key

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Configure the GEMINI_API_KEY environment variable in your R session to enable AI-enhanced PDF import and rhetorical move analysis.

```r
Sys.setenv(GEMINI_API_KEY = "your-api-key-here")
```

--------------------------------

### Fixture Metadata Information

Source: https://github.com/massimoaria/contentanalysis/blob/master/tests/testthat/fixtures/README.md

Provides sample metadata for different types of PDF fixtures, including page count, column structure, approximate character count, and expected content.

```R
# Sample characteristics (approximate)
fixtures_info <- list(
  "sample-single-column.pdf" = list(
    pages = 1,
    columns = 1,
    approx_chars = 500,
    contains = c("SAMPLE DOCUMENT", "Introduction", "Methods")
  ),
  "sample-two-columns.pdf" = list(
    pages = 1,
    columns = 2,
    approx_chars = 400,
    contains = c("TWO-COLUMN", "ABSTRACT", "Introduction")
  ),
  "sample-three-columns.pdf" = list(
    pages = 1,
    columns = 3,
    approx_chars = 200,
    contains = c("THREE-COLUMN", "Column 1", "Column 2", "Column 3")
  )
)
```

--------------------------------

### Display Citation Contexts

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Shows the first few rows of citation contexts, including the cleaned citation text, section, and the full context where the citation appears. Useful for initial data exploration.

```r
head(analysis$citation_contexts[, c("citation_text_clean", "section", "full_context")])
```

--------------------------------

### View Citation-Reference Matches

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Shows the first few citation-reference mappings, including the clean citation text, referenced authors, year, and match confidence. Useful for verifying link quality.

```r
# View citation-reference matches with confidence levels
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
head(analysis$citation_references_mapping[, c("citation_text_clean", "ref_authors",
                                               "ref_year", "match_confidence")])
#> # A tibble: 6 × 4
#>   citation_text_clean                      ref_authors ref_year match_confidence
#>   <chr>                                    <chr>       <chr>    <chr>           
#> 1 (Mitchell, 1997)                         Mitchell    1997     high            
#> 2 (Breiman, Friedman, Olshen, & Stone, 1984) Breiman     1984     high            
#> 3 (Breiman, 2001)                          Breiman, L. 2001     high            
#> 4 (see Breiman, 1996)                      Breiman, L. 1996     high            
#> 5 (Hastie, Tibshirani, & Friedman, 2009)   Hastie      2009     high            
#> 6 (Hastie et al., 2009)                    Hastie      2009     high

```

--------------------------------

### Set Gemini API Key for Hybrid Analysis

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Sets the Gemini API key as an environment variable. This is required for hybrid classification that uses the Gemini LLM.

```r
# Set your Gemini API key
Sys.setenv(GEMINI_API_KEY = "your-api-key-here")
```

--------------------------------

### Load contentanalysis Library

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Load the contentanalysis library into your R session to access its functions. This is a prerequisite for using any of the package's functionalities.

```r
library(contentanalysis)
```

--------------------------------

### Import PDF and Detect Sections Automatically

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Import a PDF document and automatically detect its sections, such as Introduction, Related Work, and References. This function normalizes references and can be configured with the number of columns and citation type.

```r
doc <- pdf2txt_auto("example_paper.pdf",
                    n_columns = 2,
                    citation_type = "author_year")
#> Using 17 sections from PDF table of contents
#> Found 15 sections: Introduction, Related work, Internal processing approaches, Random forest extra information, Visualization toolkits, Post-Hoc approaches, Size reduction, Rule extraction, Local explanation, Comparison study, Experimental design, Analysis, Conclusion, Acknowledgment, References
#> Normalized 32 references with consistent \n\n separators

# Check detected sections
names(doc)
#>  [1] "Full_text"                       "Introduction"                   
#>  [3] "Related work"                    "Internal processing approaches" 
#>  [5] "Random forest extra information" "Visualization toolkits"         
#>  [7] "Post-Hoc approaches"             "Size reduction"                 
#>  [9] "Rule extraction"                 "Local explanation"              
#> [11] "Comparison study"                "Experimental design"            
#> [13] "Analysis"                        "Conclusion"                     
#> [15] "Acknowledgment"                  "References"
```

--------------------------------

### View Parsed References

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Displays the first few rows of parsed references, showing author, year, journal, and source. Useful for a quick overview of the reference data.

```r
# View parsed references (enriched with CrossRef and OpenAlex)
head(analysis$parsed_references[, c("ref_first_author", "ref_year", "ref_journal", "ref_source")])
#>   ref_first_author ref_year           ref_journal ref_source
#> 1            Adadi     2018           IEEE Access   crossref
#> 2             <NA>     <NA>                  <NA>   crossref
#> 3           Branco     2016 ACM Computing Surveys   crossref
#> 4          Breiman     1996      Machine Learning   crossref
#> 5          Breiman     2001      Machine Learning   crossref
#> 6          Breiman     1984   International Group   crossref

```

--------------------------------

### Classify Rhetorical Moves with Rules

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Classifies rhetorical moves using predefined cue phrase dictionaries without requiring an API key. Displays sentence-level classifications.

```r
# Classify rhetorical moves using cue phrase dictionaries
Moves <- classify_rhetorical_moves(doc, use_llm = FALSE)
#> Analyzing rhetorical moves in 12 sections: Introduction, Related work, Internal processing approaches, Random forest extra information, Visualization toolkits, Post-Hoc approaches, Size reduction, Rule extraction, Local explanation, Comparison study, Analysis, Conclusion
#> Segmented 194 sentences

# Sentence-level classification
head(moves$sentences[, c("sentence_id", "section", "move", "step", "confidence")])
#> # A tibble: 6 × 5
#>   sentence_id section      move                    step               confidence
#>         <dbl> <chr>        <chr>                   <chr>                   <dbl>
#> 1           1 Introduction Unclassified            Unclassified             0   
#> 2           2 Introduction Unclassified            Unclassified             0   
#> 3           3 Introduction Unclassified            Unclassified             0   
#> 4           4 Introduction M3: Occupying the niche Announcing purpose       0.45
#> 5           5 Introduction Unclassified            Unclassified             0   
#> 6           6 Introduction Unclassified            Unclassified             0
```

--------------------------------

### Set OpenAlex API Key

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Set your OpenAlex API key in your R session to increase rate limits for heavier usage. This is optional but recommended for batch analysis.

```r
openalexR::oa_apikey("your-api-key-here")
```

--------------------------------

### Create and Display Citation Network

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Generates an interactive citation network visualization. Parameters control the maximum distance between citations, minimum connections for a node, and whether to show labels. The resulting network object can be directly displayed.

```r
# Create citation network
network <- create_citation_network(
  citation_analysis_results = analysis,
  max_distance = 800,          # Max distance between citations (characters)
  min_connections = 2,          # Minimum connections to include a node
  show_labels = TRUE
)

# Display interactive network
network
```

--------------------------------

### Check Data Sources for References

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Counts the occurrences of each reference source (e.g., 'crossref'). Helps understand the origin of the parsed reference data.

```r
# Check data sources
table(analysis$parsed_references$ref_source)
#> 
#> crossref 
#>       33

```

--------------------------------

### Ignore Large PDF Fixtures in Git

Source: https://github.com/massimoaria/contentanalysis/blob/master/tests/testthat/fixtures/README.md

Configure .gitignore to exclude large PDF files from version control while including specific smaller generated ones. This helps manage repository size.

```gitignore
# Ignore large PDF fixtures
tests/testthat/fixtures/*.pdf
!tests/testthat/fixtures/sample-*.pdf
!tests/testthat/fixtures/empty-*.pdf
```

--------------------------------

### Set Mailto for Polite Pool Access

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Configure the 'mailto' parameter to use the polite pool for CrossRef and OpenAlex APIs, enabling faster and more reliable access. This is recommended for most users.

```r
analysis <- analyze_scientific_content(
  text = doc,
  doi = "10.xxxx/xxxxx",
  mailto = "your@email.com"  # Your email for polite pool access
)
```

--------------------------------

### Analyze Citation Distribution by Section

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Shows the distribution of citations across different sections of a document. Provides counts and percentages for each section.

```r
analysis$citation_metrics$section_distribution
#> # A tibble: 14 × 3
#>    section                             n percentage
#>    <fct>                           <int>      <dbl>
#>  1 Introduction                        6      12.2 
#>  2 Related work                        9      18.4 
#>  3 Internal processing approaches      0       0   
#>  4 Random forest extra information     6      12.2 
#>  5 Visualization toolkits              4       8.16
#>  6 Post-Hoc approaches                 0       0   
#>  7 Size reduction                      6      12.2 
#>  8 Rule extraction                     3       6.12
#>  9 Local explanation                   5      10.2 
#> 10 Comparison study                    2       4.08
#> 11 Experimental design                 4       8.16
#> 12 Analysis                            4       8.16
#> 13 Conclusion                          0       0   
#> 14 Acknowledgment                      0       0

```

--------------------------------

### Hybrid Rhetorical Move Classification with Gemini

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Performs rhetorical move classification using a hybrid approach that combines rule-based methods with the Gemini LLM. Includes a progress bar for long analyses.

```r
# Hybrid classification with progress bar
Moves_hybrid <- classify_rhetorical_moves(doc, use_llm = TRUE, model = "2.5-flash")
```

--------------------------------

### Activate Rhetorical Moves in Content Analysis Pipeline

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Activates rhetorical move analysis as part of the full scientific content analysis pipeline using analyze_scientific_content().

```r
analysis <- analyze_scientific_content(
  text = doc,
  doi = "10.1016/j.mlwa.2021.100094",
  citation_type = "author_year",
  rhetorical_moves = TRUE  # Activate rhetorical analysis
)
```

--------------------------------

### Create Citation Network with Custom Min Connections

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Generates a citation network highlighting only highly connected citations by setting a large `max_distance` and a high `min_connections`. This is useful for identifying central or influential citations.

```r
# Show only highly connected citations
network_hubs <- create_citation_network(
  analysis,
  max_distance = 1000,
  min_connections = 5
)
```

--------------------------------

### View Enriched OpenAlex Metadata

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Displays the first few rows of enriched OpenAlex metadata for references, including title, publication year, citation count, type, and OpenAlex status. Requires OpenAlex data to be retrieved.

```r
# If OpenAlex data was retrieved
if (!is.null(analysis$references_oa)) {
  # View enriched metadata
  head(analysis$references_oa[, c("title", "publication_year", "cited_by_count",
                                   "type", "oa_status")])

  # Analyze citation impact
  summary(analysis$references_oa$cited_by_count)
}
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#>    101.0    207.2   1153.5  12252.6   5411.2 123905.0

```

--------------------------------

### Aggregate Rhetorical Move Blocks

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Aggregates consecutive sentences with the same rhetorical move into blocks. Displays aggregated move block information.

```r
# Aggregated move blocks (consecutive sentences with the same move)
head(moves$move_blocks[, c("block_id", "section", "move", "n_sentences", "avg_confidence")])
#> # A tibble: 6 × 5
#>   block_id section      move                         n_sentences avg_confidence
#>      <dbl> <chr>        <chr>                              <dbl>          <dbl>
#> 1        1 Introduction Unclassified                           3           0   
#> 2        2 Introduction M3: Occupying the niche                1           0.45
#> 3        3 Introduction Unclassified                           3           0   
#> 4        4 Introduction M2: Establishing a niche               1           0.35
#> 5        5 Introduction M1: Establishing a territory           1           0.65
#> 6        6 Introduction Unclassified                           6           0
```

--------------------------------

### Rhetorical Move Distribution Summary

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Calculates and displays the distribution of rhetorical moves across different sections of a document.

```r
# Move distribution across sections
Moves$summary$move_distribution
#> # A tibble: 20 × 4
#>    section                move                             n   pct
#>    <chr>                  <chr>                        <int> <dbl>
#>  1 Analysis               M1: Establishing a territory     3  42.9
#>  2 Analysis               M2: Establishing a niche         1  14.3
#>  3 Analysis               M3: Occupying the niche          3  42.9
#>  4 Comparison study       M3: Occupying the niche          1 100  
#>  5 Conclusion             M2: Evaluating the study         2  66.7
#>  6 Conclusion             M3: Looking forward              1  33.3
#>  7 Introduction           M1: Establishing a territory     2  28.6
#>  8 Introduction           M2: Establishing a niche         2  28.6
#>  9 Introduction           M3: Occupying the niche          3  42.9
#> 10 Local explanation      M1: Establishing a territory     6  85.7
#> 11 Local explanation      M2: Establishing a niche         1  14.3
#> 12 Post-Hoc approaches    M3: Occupying the niche          1 100  
#> 13 Related work           M1: Establishing context         1  50  
#> 14 Related work           M2: Reviewing prior work         1  50  
#> 15 Rule extraction        M1: Establishing a territory     1  33.3
#> 16 Rule extraction        M2: Establishing a niche         2  66.7
#> 17 Size reduction         M1: Establishing a territory     2  40  
#> 18 Size reduction         M2: Establishing a niche         2  40  
#> 19 Size reduction         M3: Occupying the niche          1  20  
#> 20 Visualization toolkits M1: Establishing a territory     3 100
```

--------------------------------

### Create Citation Network with Hidden Labels

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Generates a citation network visualization with labels turned off using `show_labels = FALSE`. This can result in a cleaner visual representation when focusing on the network structure rather than individual citation labels.

```r
# Hide labels for cleaner visualization
network_clean <- create_citation_network(
  analysis,
  show_labels = FALSE
)
```

--------------------------------

### View Hybrid Analysis Rhetorical Flow

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Visualizes the rhetorical flow of a paper after performing hybrid classification using the Gemini LLM.

```r
# View the rhetorical flow of the paper
Moves_hybrid$summary$flow_pattern
```

--------------------------------

### Analyze Match Quality Distribution

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Counts the occurrences of different match confidence levels for citation-reference links. Helps assess the reliability of the mappings.

```r
# Match quality distribution
table(analysis$citation_references_mapping$match_confidence)
#> 
#>            high no_match_author 
#>              41               8

```

--------------------------------

### Exclude PDF Fixtures from Built Package

Source: https://github.com/massimoaria/contentanalysis/blob/master/tests/testthat/fixtures/README.md

Configure .Rbuildignore to prevent large or real-world PDF fixtures from being included in the final package distribution. This ensures the package remains lean.

```regex
^tests/testthat/fixtures/large-.*\.pdf$
^tests/testthat/fixtures/.*-real\.pdf$
```

--------------------------------

### Generate Citation Cluster Descriptions

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Use this function to generate thematic descriptions for each section's bibliography based on TF-IDF analysis of reference titles. View the top terms per section or detailed TF-IDF scores.

```r
# Generate cluster descriptions
cluster_desc <- describe_citation_clusters(analysis, top_n = 10)

# View summary: top terms per section
cluster_desc$cluster_summary
#> # A tibble: 10 × 3
#>    section                         n_references top_terms                       
#>    <chr>                                  <int> <chr>                           
#>  1 Introduction                               5 learning, bagging, bagging pred…
#>  2 Related work                               7 interpretable machine, interpre…
#>  3 Random forest extra information            5 forests, random forests, annals…
#>  4 Visualization toolkits                     4 tree, forests, random forests, …
#>  5 Size reduction                             4 adaptive, adaptive diagnostic, …
#>  6 Rule extraction                            2 annals applied, applied, applie…
#>  7 Local explanation                          2 box classifiers, classification…
#>  8 Comparison study                           1 annals applied, applied, applie…
#>  9 Experimental design                        3 bell, bell laboratories, labora…
#> 10 Analysis                                   3 acm computing, computing survey…

# View detailed TF-IDF scores
cluster_desc$cluster_descriptions
#> # A tibble: 83 × 7
#>    section      ngram                ngram_size     n    tf   idf tf_idf
#>    <chr>        <chr>                     <int> <int> <dbl> <dbl>  <dbl>
#>  1 Introduction learning                      1     2  0.08  1.61 0.129 
#>  2 Introduction bagging                       1     1  0.04  2.30 0.0921
#>  3 Introduction bagging predictors            2     1  0.04  2.30 0.0921
#>  4 Introduction belmont                       1     1  0.04  2.30 0.0921
#>  5 Introduction belmont wadsworth             2     1  0.04  2.30 0.0921
#>  6 Introduction data                          1     1  0.04  2.30 0.0921
#>  7 Introduction data mining                   2     1  0.04  2.30 0.0921
#>  8 Introduction elements                      1     1  0.04  2.30 0.0921
#>  9 Introduction elements statistical          2     1  0.04  2.30 0.0921
#> 10 Introduction inference                     1     1  0.04  2.30 0.0921
#> # ℹ 73 more rows
```

--------------------------------

### Access Rhetorical Moves Results from Pipeline

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Accesses the results of the rhetorical move analysis, including sentences, move blocks, and summary statistics, after it has been run as part of the full content analysis pipeline.

```r
# Access results
analysis$rhetorical_moves$sentences
analysis$rhetorical_moves$move_blocks
analysis$rhetorical_moves$summary
```

--------------------------------

### Perform Comprehensive Scientific Content Analysis

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Analyze scientific content by extracting citations, retrieving metadata from CrossRef and OpenAlex, matching citations to references, and performing text analysis. Requires the document text, DOI, and an email address for API requests.

```r
analysis <- analyze_scientific_content(
  text = doc,
  doi = "10.1016/j.mlwa.2021.100094",
  mailto = "your@email.com",
  citation_type = "author_year"
)
#> Extracting author-year citations only
#> Attempting to retrieve references from CrossRef...
#> Successfully retrieved 33 references from CrossRef
#> Fetching Open Access metadata for 14 DOIs from OpenAlex...
#> Successfully retrieved metadata for 14 references from OpenAlex
#> Enriching CrossRef references with 32 PDF-parsed entries...
#> Enriched 10 CrossRef references with PDF-parsed data
```

--------------------------------

### View Summary Statistics of Content Analysis

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Access the summary statistics generated by the analyze_scientific_content function. This includes metrics like total words, unique words, citation counts, lexical diversity, and reference matching quality.

```r
analysis$summary
#> $total_words_analyzed
#> [1] 3230
#> 
#> $unique_words
#> [1] 1238
#> 
#> $citations_extracted
#> [1] 49
#> 
#> $narrative_citations
#> [1] 15
#> 
#> $parenthetical_citations
#> [1] 34
#> 
#> $complex_citations_parsed
#> [1] 12
#> 
#> $lexical_diversity
#> [1] 0.3832817
#> 
#> $average_citation_context_length
#> [1] 2856.061
#> 
#> $citation_density_per_1000_words
#> [1] 6.83
#> 
#> $references_parsed
#> [1] 33
#> 
#> $citations_matched_to_refs
#> [1] 41
#> 
#> $match_quality
#> # A tibble: 2 × 3
#>   match_confidence     n percentage
#>   <chr>            <int>      <dbl>
#> 1 high                41       83.7
#> 2 no_match_author      8       16.3
#> 
#> $citation_type_used
#> [1] "author_year"
```

--------------------------------

### Create Citation Network with Custom Max Distance

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Generates a citation network focusing only on very close citations by setting a small `max_distance` and a low `min_connections`. This helps to visualize immediate citation relationships.

```r
# Focus on very close citations only
network_close <- create_citation_network(
  analysis,
  max_distance = 300,
  min_connections = 1
)
```

--------------------------------

### Examine Most Frequent Words

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Display the top 10 most frequent words from the analysis results. This snippet shows the structure of the word frequencies output, including word, count, frequency, and rank.

```r
head(analysis$word_frequencies, 10)
```

--------------------------------

### Access Network Statistics

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Retrieves and displays statistics from a generated citation network. This includes the number of nodes and edges, average distance between citations, distribution of citations by section, and a list of citations appearing in multiple sections.

```r
stats <- attr(network, "stats")

# Network size
cat("Nodes:", stats$n_nodes, "\n")
cat("Edges:", stats$n_edges, "\n")
cat("Average distance:", stats$avg_distance, "characters\n")

# Citations by section
print(stats$section_distribution)

# Multi-section citations
if (nrow(stats$multi_section_citations) > 0) {
  print(stats$multi_section_citations)
}
```

--------------------------------

### Analyze Citation Co-occurrence

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

View the initial data for citation co-occurrence analysis. This snippet displays the first 6 rows of network data, showing pairs of citations, their distance, and types.

```r
head(analysis$network_data)
```

--------------------------------

### Examine Citation Type Distribution

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Analyze the distribution of different citation types found in the document. This helps understand how authors cite references, distinguishing between narrative, parenthetical, and other forms.

```r
analysis$citation_metrics$type_distribution
#> # A tibble: 9 × 3
#>   citation_type                   n percentage
#>   <chr>                       <int>      <dbl>
#> 1 parsed_from_multiple           12      24.5 
#> 2 author_year_basic               9      18.4 
#> 3 author_year_and                 8      16.3 
#> 4 narrative_etal                  7      14.3 
#> 5 author_year_etal                3       6.12
#> 6 narrative_three_authors_and     3       6.12
#> 7 narrative_two_authors_and       3       6.12
#> 8 narrative_four_authors_and      2       4.08
#> 9 see_citations                   2       4.08
```

--------------------------------

### Track Methodological Terms

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Identify and count occurrences of predefined methodological terms within a document. This snippet requires a document object and a vector of terms to track.

```r
method_terms <- c("machine learning", "regression", "validation", "dataset")
word_dist <- calculate_word_distribution(doc, method_terms)
```

--------------------------------

### Calculate Readability Indices

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Compute various readability indices for a given text, such as Flesch-Kincaid grade level and Gunning Fog index. The 'detailed' argument provides additional metrics like sentence length and syllable counts.

```r
readability <- calculate_readability_indices(doc$Full_text, detailed = TRUE)
readability
#> # A tibble: 1 × 12
#>   flesch_kincaid_grade flesch_reading_ease automated_readability_index
#>                  <dbl>               <dbl>                       <dbl>
#> 1                 12.5                34.2                        11.9
#> # ℹ 9 more variables: gunning_fog_index <dbl>, n_sentences <int>,
#> #   n_words <int>, n_syllables <dbl>, n_characters <int>,
#> #   n_complex_words <int>, avg_sentence_length <dbl>,
#> #   avg_syllables_per_word <dbl>, pct_complex_words <dbl>
```

--------------------------------

### Find Citations to Specific Authors

Source: https://github.com/massimoaria/contentanalysis/blob/master/README.md

Filters citation-reference mappings to find citations attributed to a specific author (e.g., 'Smith'). Returns the clean citation text, full reference text, and match confidence.

```r
# Find all citations to works by Smith
analysis$citation_references_mapping %>%
  filter(grepl("Smith", ref_authors, ignore.case = TRUE)) %>%
  select(citation_text_clean, ref_full_text, match_confidence)
#> # A tibble: 0 × 3
#> # ℹ 3 variables: citation_text_clean <chr>, ref_full_text <chr>, match_confidence <chr>

```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.