Software Built For Your Research
StatsWithR develops statistical software for researchers, statisticians, data managers, study coordinators, analysts, instructors, and other professionals who work with research data. Our applications are designed to address time-consuming tasks that often occur before, during, and after statistical analysis.
Available tools support areas such as data-quality review, research-data cleaning, statistical-output parsing, publication-table preparation, data-dictionary auditing, documentation, and reproducible workflow management. Future applications will continue to focus on practical problems that researchers and analysts regularly encounter.
The goal is to provide focused tools that make repetitive research tasks more organized, transparent, and efficient. As we know, research projects often involve much more than selecting and running a statistical model. Before analysis begins, data will need to be reviewed for missingness, inconsistent coding, unusual values, duplicate records, structural problems, and documentation gaps.
After analysis, statistical output may need to be reorganized, checked against its source, formatted consistently, and converted into clean tables for manuscripts, reports, presentations, or internal review.
StatsWithR applications are developed around these practical stages of the research process. Depending on the application, users may be able to:
Audit research-data structures and documentation
Identify potential data-quality problems
Apply transparent and reviewable data corrections
Review REDCap data dictionaries before data collection
Extract tables from statistical-software output
Standardize statistical labels and formatting
Export results into commonly used research formats
The StatsWithR research focused statistical software suite gives researchers browser-based applications for a variety of purposes including: publication-ready tables from raw statistical output, REDCap dictionary auditing, and research data quality management tools. The subscription includes all current paid applications, newly released paid applications, and ongoing software improvements.
Current Application Areas
Research Data Quality
Applications in this area help users profile, audit, clean, and document research datasets. They are designed to make potential problems easier to identify before those problems affect statistical analysis, reporting, or publication.
Supported workflows may include variable-type review, missing-data assessment, category standardization, range checks, duplicate detection, date validation, study-specific quality rules, documented corrections, and cleaned-data export.
Research Data Dictionaries
Data-dictionary applications support structured review before a project begins collecting or analyzing data. These tools can help identify problems involving field definitions, coded choices, validation settings, branching logic, identifiers, formatting, and analysis readiness.
The purpose is to address preventable structural problems early, while corrections are still easier to make.
Statistical-Output and Publication Tables
Publication-table applications help convert statistical-software output into organized and editable table collections.
These tools are designed for output from widely used statistical environments, including programming-based, command-based, and point-and-click software. Users can review extracted values against retained source material, edit labels and structure, apply consistent formatting, and export tables for Word, Excel, HTML, CSV, or PDF-oriented workflows.
Additional Applications in Development
Future releases will continue to focus on research workflow problems where a specialized application can reduce repetitive work, improve documentation, or make an important quality-control step easier to complete.
Newly released paid applications will be added to the active subscription. Subscribers will not need to purchase each newly released paid application separately.