consumeR: Transparent and Reproducible Consumer Research Analysis
Abstract
This release represents the first public, citable version of the package submitted for review to the Journal of Open Source Software (JOSS). The goal of this release is not feature completeness, but to establish a stable, well-documented, and reproducible core workflow suitable for peer review and long-term extension. What this release does Implements a fully reproducible end-to-end workflow for common marketing and consumer psychology research tasks, from data ingestion and cleaning through statistical analysis and reporting. Provides opinionated defaults that prioritize transparency, consistency, and reviewer-friendly outputs over flexibility. Enforces fail-fast validation (e.g., required variables, scale construction, missingness handling) to prevent silent analytic errors. Produces publication-ready outputs (tables, diagnostics, and summaries) designed to be copied directly into manuscripts and supplements. Adheres to tidyverse-consistent conventions and explicit namespace usage to reduce ambiguity and masking issues. Includes comprehensive documentation and examples sufficient for an independent researcher to reproduce analyses without access to the original study context. What this release deliberately does not do It does not attempt to cover every statistical method used in marketing or consumer research. It does not introduce novel statistical techniques; the contribution is workflow standardization and reproducibility. It does not guarantee backward compatibility beyond this release (pre-1.0 semantic versioning applies). Why this version exists This version exists to: Provide a stable reference point for JOSS review and citation. Lock in a transparent analytical philosophy before additional features and integrations are added. Enable external reviewers to evaluate the package on clarity, reproducibility, documentation quality, and scientific usefulness, rather than feature breadth. Planned future development (non-binding) Future releases may expand: Supported statistical models and diagnostics API and data-source integrations Automated reporting and visualization layers Modular extensions for domain-specific workflows These are intentionally excluded from v0.1.0 to keep the initial submission focused and reviewable. Versioning statement This release is tagged v0.1.0 to signal: A stable, review-ready core An evolving public API Active development following JOSS review
Fields of Study & Subjects
Scientific Computing and Data Management
Data Analysis with R
Research Data Management Practices
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 January 2026