RBiblioSynth is a comprehensive modular R framework for IEEE Q1 Journal-quality bibliometric analysis and systematic review automation. It provides:

• M0 Data Orchestrator: Multi-source loading, merging, PRISMA automation
• M1 Main Information: Descriptive analysis, author metrics, citation networks, topic modeling
• M2 Annual Production: 30+ growth models, 8 forecasting methods, changepoint detection
• M3 Countries: Spatial statistics, economic correlations, collaboration networks

The framework combines Bibliometrix (R), custom R6 classes, and rigorous statistical methods to produce IEEE-style outputs (plots, tables, LaTeX reports).

| Feature | bibliometrix | RBiblioSynth | |---------|--------------|--------------||Growth Models | 2-3 | 30+ (Bass, Gompertz, Weibull, Richards, von Bertalanffy, MMF) | | Forecasting | Basic | 8 methods (ARIMA, SARIMA, ETS, TBATS, Prophet, State Space, Ensemble) | | Spatial Statistics | None | Full suite (Moran's I, Geary's C, LISA, Getis-Ord Gi*) | | Economic Correlation | None | GDP, HDI, R&D integration | | Bootstrap CIs | None | BCA method for all metrics | | Hypothesis Tests | Limited | 40+ formal tests with FDR correction |

1. 30+ Growth Models: Bass diffusion, Gompertz, Weibull, Richards, von Bertalanffy, MMF with comparison
2. Ensemble Forecasting: AIC-weighted model averaging
3. Spatial Bibliometrics: First implementation of Moran's I, LISA for bibliometric data
4. PRISMA Automation: Automatic PRISMA 2020 flow diagrams from JSON/YAML specs
5. Bootstrap Confidence Intervals: BCA method for robust inference
6. QAP Network Tests: Statistical significance for collaboration networks

# Load package
source("R/core/bootstrap.R")

# Define data sources
sources <- list(
  scopus = list(file = "data/scopus.bib", db = "scopus", format = "bibtex")
)

# Run M0 Data Orchestrator
m0_result <- run_m0(sources)
bib_data <- m0_get_bib_data(m0_result)

# Run M1 Main Information
m1_result <- run_m1(bib_data)

# Run M2 Annual Production
annual_data <- m0_get(m0_result, "annual")
m2_result <- run_m2(annual_data)

# Run M3 Countries
m3_result <- run_m3(bib_data)

• Multi-source loading (Scopus, WoS, OpenAlex, Generic CSV)
• Automatic deduplication (DOI + fuzzy title matching)
• PRISMA 2020 diagram generation
• Quality validation (ORCID, DOI, email extraction)

• Author productivity and indices (h-index, g-index, m-quotient, i10)
• Citation analysis with distribution fitting
• Topic modeling (LDA with coherence/perplexity)
• Bradford's Law and Lotka's Law analysis
• Keyword co-occurrence networks
• Kleinberg burst detection

• 30+ Growth Models: Bass, Gompertz, Weibull, Richards, von Bertalanffy, MMF
• 8 Forecasting Methods: ARIMA, SARIMA, ETS, TBATS, Prophet, State Space, Naive, Ensemble
• Changepoint Detection: PELT, CUSUM, Binary Segmentation
• Harmonic Analysis: FFT, Lomb-Scargle
• Model Diagnostics: AIC/BIC comparison, cross-validation
• 15+ Statistical Tests: Normality, autocorrelation, heteroscedasticity

• Spatial Statistics: Moran's I, Geary's C, LISA, Getis-Ord Gi*
• Economic Correlation: GDP, HDI, R&D expenditure
• Collaboration Indices: Salton, Jaccard, Affinity
• Temporal Dynamics: Rank mobility, Markov transitions, NELSOP
• QAP Network Tests: Statistical significance for correlations
• 12 Formal Hypothesis Tests

• M0: Data Orchestrator (load, merge, organize sources; PRISMA diagram & report)
• M1: Main Information (overview, doc types, authors, citations, countries, keywords, Bradford, Lotka)
• M2: Annual Production (30+ growth models, forecasting, changepoint, diagnostics)
• M3: Countries (spatial stats, economic correlation, QAP tests, temporal dynamics)
• M4: Institutions (collaboration networks, quadrants, indicators)
• M5: Authors & Documents (h-index evolution, citation networks)
• M6: Clustering & Themes (co-word, Louvain, topic evolution)
• M7: Conceptual & Social Structure (collaboration networks, betweenness)
• M8: Automated Bibliometric Report (JSON + CSV + plots)

• M9: LLM Report Generator (combine bibliometric insights with text analysis)
• M10: Zotero Integration (tagging, notes, thematic structuring)

All modules include:

• Bootstrap Confidence Intervals: BCA method for all statistics
• Multiple Testing Correction: FDR (Benjamini-Hochberg)
• Effect Sizes: Cohen's d, eta-squared
• Power Analysis: Sample size considerations
• Formal Hypothesis Tests: 40+ tests with interpretations

# Treemap for hierarchical display
render_m1_treemap(m1_data, type = "countries")

# Sankey diagram for collaboration flows
render_m3_sankey(m3_data, type = "country")

# Three-field plot (Authors-Keywords-Sources)
render_m1_three_field(data)

# Time series with forecast bands
m2_result$artifacts$plots$forecasting

# From GitHub
devtools::install_github("yourusername/RBiblioSynth")
library(RBiblioSynth)

@article{rbibliosynth2026,
  title = {RBiblioSynth: A Comprehensive R Framework for Bibliometric Analysis},
  author = {Mayorga},
  journal = {SoftwareX},
  year = {2026},
  doi = {10.1016/j.softx.2026.XXXXX}
}

MIT License

This package extends bibliometrix with advanced statistical methods and rigorously tested implementations suitable for IEEE Q1 journal publications.