litdiscover

A Stata package for theory-aware literature review, analysis, and discovery.

litdiscover combines latent Dirichlet allocation (LDA) topic modelling of abstract text with a deductive overlay of researcher-coded construct fields (theory, dependent variable, independent variable, moderator, mediator, decision, context, method, journal, year). It produces topic-by-field cross-tabulations, TCCM and ADO framework outputs, network-analytic measures over construct co-occurrences, FREX exclusivity scores, per-topic stability diagnostics, UMass coherence, and a suite of static figures and interactive HTML deliverables. The package is designed for systematic literature reviews across management, marketing, organisation studies, information systems, education, health policy, and the broader social and behavioural sciences.

The short-form alias litdi is provided as a convenience.

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Installation

litdiscover is currently distributed via GitHub and SSC distribution

Inside Stata:

ssc install litdiscover

To install from this repository, run inside Stata:

net install litdiscover, from("https://raw.githubusercontent.com/Davcik/litdiscover/main/")

You will additionally need:

• Python 3.10 or later configured for use with Stata (see help python). The package uses pandas, numpy, scikit-learn, scipy, and networkx by default; for the figures and interactive options, also matplotlib, seaborn, wordcloud, pyLDAvis, pyvis, and plotly.
• SSC packages heatplot, palettes, and colrspace (only when the figures option is used):

ssc install heatplot
ssc install palettes
ssc install colrspace

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Documentation

For full reference documentation (every option, every output file, every returned scalar and macro, and the complete reference list), run help litdiscover inside Stata after installation.

For a research-question-organised guide with seven worked examples, see USECASES.md.

For the version history, see CHANGELOG.md.

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Example dataset

The repository includes litdiscover_example500.dta, a 500-document synthetic corpus covering five theoretical perspectives and ten journals over 2008–2025, constructed to mirror the statistical structure of a real systematic literature review corpus. It is intended for documentation, examples, and testing; for research use, replace it with your own coded corpus.

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Citation

When citing litdiscover in academic work, please use:

Davcik, N. S. 2026. LITDISCOVER: Stata module for theory-aware literature review, analysis, and discovery. Statistical Software Components S459718, Boston College Department of Economics, Available at: https://ideas.repec.org/c/boc/bocode/s459718.html

A CITATION.cff file is provided in the repository root for GitHub's "Cite this repository" feature and for ingestion by reference managers such as Zotero, Mendeley, and JabRef.

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Licence

litdiscover is released under the GNU General Public License version 3 or later (GPL-3.0-or-later). You may redistribute and modify it under the terms of that licence; modified versions and larger works that incorporate litdiscover must also be released under GPL-3 or later. See LICENSE for the full text.

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.

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Author

Nebojsa S. Davcik EM Normandie Business School, Oxford, UK ORCID: 0000-0003-1041-8788 Email: davcik@live.com

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Contributing

Issue reports and feature requests are welcome via the GitHub issue tracker. For substantive proposals (new options, new output schemas, changes to the package's API), please open an issue for discussion before submitting a pull request.